data mining essay questions

Presentations made painless

Get Premium

105 Data Mining Essay Topic Ideas & Examples

Inside This Article

Data mining is a powerful tool that helps businesses and organizations uncover hidden patterns, trends, and insights from large datasets. It involves the process of extracting valuable information from raw data, which can then be used for various purposes such as improving decision-making, predicting future outcomes, and understanding customer behavior. If you are a student or a professional looking to write an essay on data mining, here are 105 topic ideas and examples to help you get started.

The importance of data mining in today's business world
Ethical considerations in data mining
The impact of data mining on privacy
How data mining is used in healthcare to improve patient outcomes
Predictive analytics: Using data mining to forecast future trends
Data mining techniques for fraud detection in financial institutions
The role of data mining in customer relationship management
The use of data mining in social media marketing
Data mining and its application in personalized advertising
The benefits of data mining in supply chain management
Text mining: Analyzing unstructured data to extract valuable insights
The challenges of big data mining
Data mining in e-commerce: Enhancing customer experience
The role of data mining in improving cybersecurity
Data mining and its impact on decision-making in organizations
The use of data mining in predicting stock market trends
Data mining and its role in recommendation systems
The benefits of data mining in the education sector
Data mining techniques for sentiment analysis
The ethical implications of data mining in government surveillance
Data mining in the gaming industry: Enhancing player experience
The role of data mining in personalized medicine
Data mining techniques for credit scoring and risk assessment
The use of data mining in sports analytics
Data mining and its impact on urban planning
Data mining and its role in weather forecasting
The challenges of data mining in social network analysis
Data mining techniques for detecting plagiarism in academic papers
Data mining and its application in predicting natural disasters
The role of data mining in improving transportation systems
Data mining and its impact on online dating platforms
Data mining for predicting customer churn in telecommunications industry
The use of data mining in optimizing energy consumption
Data mining techniques for detecting credit card fraud
Data mining and its role in personalized news recommendation
The benefits of data mining in human resources management
Data mining in healthcare for disease diagnosis and treatment
Data mining and its impact on online advertising
Data mining techniques for identifying patterns in gene expression data
The role of data mining in improving online learning platforms
Data mining and its application in criminal investigations
The use of data mining in optimizing manufacturing processes
Data mining techniques for predicting customer lifetime value
The benefits of data mining in predicting traffic congestion
Data mining and its role in predicting customer preferences
Data mining in environmental analysis and conservation efforts
Data mining and its impact on personalized financial planning
The challenges of data mining in healthcare data integration
Data mining techniques for analyzing social media sentiment
The role of data mining in improving public safety
Data mining and its application in fraud detection in insurance industry
The use of data mining in optimizing online search engines
Data mining techniques for predicting student performance in education
Data mining and its impact on improving online user experience
Data mining and its role in predicting customer satisfaction
The benefits of data mining in optimizing logistics and supply chain
Data mining in crime analysis and prevention
Data mining and its impact on personalization in online shopping
Data mining techniques for analyzing customer feedback and reviews
The role of data mining in improving healthcare resource allocation
Data mining and its application in predicting customer lifetime loyalty
The use of data mining in optimizing inventory management
Data mining techniques for detecting fraudulent insurance claims
Data mining and its role in predicting disease outbreaks
Data mining in sentiment analysis of political discourse
Data mining and its impact on improving online voting systems
The challenges of data mining in analyzing geospatial data
Data mining techniques for optimizing pricing strategies in retail
The benefits of data mining in predicting customer churn in telecom industry
Data mining and its role in improving road safety
Data mining and its application in predicting customer behavior
The use of data mining in optimizing energy distribution networks
Data mining techniques for detecting insider trading in financial markets
Data mining and its impact on personalized travel recommendations
Data mining and its role in predicting customer loyalty
The benefits of data mining in optimizing warehouse operations
Data mining in fraud detection and prevention in online transactions
Data mining and its impact on personalized healthcare recommendations
Data mining techniques for analyzing customer segmentation
The role of data mining in improving disaster response and recovery
Data mining and its application in predicting customer lifetime value
The use of data mining in optimizing fleet management
Data mining techniques for detecting money laundering activities
Data mining and its role in predicting customer preferences in online advertising
The benefits of data mining in optimizing service quality in hospitality industry
Data mining in predicting student dropout and improving retention
Data mining and its impact on personalized music recommendations
Data mining techniques for analyzing patterns in web usage data
The role of data mining in improving urban mobility and transportation systems
Data mining and its application in predicting customer satisfaction in retail
The use of data mining in optimizing healthcare resource allocation
Data mining techniques for detecting online identity theft
Data mining and its role in predicting customer lifetime loyalty in e-commerce
The benefits of data mining in optimizing delivery routes
Data mining in detecting patterns of online extremist behavior
Data mining and its impact on enhancing personalized learning experiences
Data mining techniques for analyzing customer churn in subscription-based services
The role of data mining in improving disaster risk reduction strategies
Data mining and its application in predicting customer behavior in online gaming
The use of data mining in optimizing maintenance schedules for industrial equipment
Data mining techniques for detecting healthcare fraud and abuse
Data mining and its role in predicting customer preferences in online travel booking
The benefits of data mining in optimizing waste management processes
Data mining in detecting patterns of cyberbullying behavior
Data mining and its impact on enhancing personalized financial advice

These topic ideas provide a wide range of options for your data mining essay. Whether you are interested in business applications, healthcare, social media, or any other field, there is a topic that suits your interests. Remember to choose a topic that you are passionate about and conduct thorough research to provide a well-informed and insightful essay on data mining.

Want to create a presentation now?

Instantly Create A Deck

Let PitchGrade do this for me

Hassle Free

We will create your text and designs for you. Sit back and relax while we do the work.

Explore More Content

Trending Now
Foundational Courses
Data Science
Practice Problem
Machine Learning
System Design
DevOps Tutorial
Top 50+ Python Interview Questions and Answers (Latest 2024)
Top 50 Plus Interview Questions for Statistics with Answers 2023
Top 10 Traditional HR Interview Questions and Answers
TCS Digital Interview Questions
Top 50 SEO Interview Questions [2024]
Happiest Minds Technology Interview Experience (On-Campus)
TCS Interview Experience | Set 5 (On-campus)
DELL Interview | Set 1 (On-Campus)
TCS Ninja Interview Experience 2022
TCS Interview Experience | Set 41 (On-Campus)
Salesforce Interview Questions for Technical Profiles
Sirion Interview Experience for ASE (On-Campus)
Microsoft Interview | Set 5
Ford Motor Interview Experience (On-Campus)
Happiest Minds Interview Experience 2023
MoneyTap Interview Experience (On-Campus 2020)
MindTree Interview Experience (On-Campus) 2023
Analytics Quotient Interview Experience | On-Campus
Micron Technologies Interview Experience for FTE (On-Campus)

Top 50 Data Mining Interview Questions & Answers

Data Mining is a process of extracting useful information from data warehouses or from bulk data. This article contains the Most Popular and Frequently Asked Interview Questions of Data Mining along with their detailed answers. These will help you to crack any interview for a data scientist job. So let’s get started.

1. What is Data Mining?

Data mining refers to extracting or mining knowledge from large amounts of data. In other words, Data mining is the science, art, and technology of discovering large and complex bodies of data in order to discover useful patterns.

2. What are the different tasks of Data Mining?

The following activities are carried out during data mining:

Classification
Association Rule Discovery
Sequential Pattern Discovery
Deviation Detection

3. Discuss the Life cycle of Data Mining projects?

The life cycle of Data mining projects:

Business understanding: Understanding projects objectives from a business perspective, data mining problem definition.
Data understanding: Initial data collection and understand it.
Data preparation: Constructing the final data set from raw data.
Modeling: Select and apply data modeling techniques.
Evaluation: Evaluate model, decide on further deployment.
Deployment : Create a report, carry out actions based on new insights.

4. Explain the process of KDD?

Data mining treat as a synonym for another popularly used term, Knowledge Discovery from Data, or KDD. In others view data mining as simply an essential step in the process of knowledge discovery, in which intelligent methods are applied in order to extract data patterns.

Knowledge discovery from data consists of the following steps:

Data cleaning (to remove noise or irrelevant data).
Data integration (where multiple data sources may be combined).
Data selection (where data relevant to the analysis task are retrieved from the database).
Data transformation (where data are transmuted or consolidated into forms appropriate for mining by performing summary or aggregation functions, for sample).
Data mining (an important process where intelligent methods are applied in order to extract data patterns).
Pattern evaluation (to identify the fascinating patterns representing knowledge based on some interestingness measures).
Knowledge presentation (where knowledge representation and visualization techniques are used to present the mined knowledge to the user).

5. What is Classification?

Classification is the processing of finding a set of models (or functions) that describe and distinguish data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown. Classification can be used for predicting the class label of data items. However, in many applications, one may like to calculate some missing or unavailable data values rather than class labels.

6. Explain Evolution and deviation analysis?

Data evolution analysis describes and models regularities or trends for objects whose behavior variations over time. Although this may involve discrimination, association, classification, characterization, or clustering of time-related data, distinct features of such an analysis involve time-series data analysis, periodicity pattern matching, and similarity-based data analysis.

In the analysis of time-related data, it is often required not only to model the general evolutionary trend of the data but also to identify data deviations that occur over time. Deviations are differences between measured values and corresponding references such as previous values or normative values. A data mining system performing deviation analysis, upon the detection of a set of deviations, may do the following: describe the characteristics of the deviations, try to describe the reason behindhand them, and suggest actions to bring the deviated values back to their expected values.

7. What is Prediction?

Prediction can be viewed as the construction and use of a model to assess the class of an unlabeled object, or to measure the value or value ranges of an attribute that a given object is likely to have. In this interpretation, classification and regression are the two major types of prediction problems where classification is used to predict discrete or nominal values, while regression is used to predict incessant or ordered values.

8. Explain the Decision Tree Classifier?

A Decision tree is a flow chart-like tree structure, where each internal node (non-leaf node) denotes a test on an attribute, each branch represents an outcome of the test and each leaf node (or terminal node) holds a class label. The topmost node of a tree is the root node.

A Decision tree is a classification scheme that generates a tree and a set of rules, representing the model of different classes, from a given data set. The set of records available for developing classification methods is generally divided into two disjoint subsets namely a training set and a test set. The former is used for originating the classifier while the latter is used to measure the accuracy of the classifier. The accuracy of the classifier is determined by the percentage of the test examples that are correctly classified.

In the decision tree classifier, we categorize the attributes of the records into two different types. Attributes whose domain is numerical are called the numerical attributes and the attributes whose domain is not numerical are called categorical attributes. There is one distinguished attribute called a class label. The goal of classification is to build a concise model that can be used to predict the class of the records whose class label is unknown. Decision trees can simply be converted to classification rules.

9. What are the advantages of a decision tree classifier?

Decision trees are able to produce understandable rules.
They are able to handle both numerical and categorical attributes.
They are easy to understand.
Once a decision tree model has been built, classifying a test record is extremely fast.
Decision tree depiction is rich enough to represent any discrete value classifier.
Decision trees can handle datasets that may have errors.
Decision trees can deal with handle datasets that may have missing values.
They do not require any prior assumptions. Decision trees are self-explanatory and when compacted they are also easy to follow. That is to say, if the decision tree has a reasonable number of leaves it can be grasped by non-professional users. Furthermore, since decision trees can be converted to a set of rules, this sort of representation is considered comprehensible.

10. Explain Bayesian classification in Data Mining?

A Bayesian classifier is a statistical classifier. They can predict class membership probabilities, for instance, the probability that a given sample belongs to a particular class. Bayesian classification is created on the Bayes theorem. A simple Bayesian classifier is known as the naive Bayesian classifier to be comparable in performance with decision trees and neural network classifiers. Bayesian classifiers have also displayed high accuracy and speed when applied to large databases.

11. Why Fuzzy logic is an important area for Data Mining?

Rule-based systems for classification have the disadvantage that they involve exact values for continuous attributes. Fuzzy logic is useful for data mining systems performing classification. It provides the benefit of working at a high level of abstraction. In general, the usage of fuzzy logic in rule-based systems involves the following:

Attribute values are changed to fuzzy values.
For a given new sample, more than one fuzzy rule may apply. Every applicable rule contributes a vote for membership in the categories. Typically, the truth values for each projected category are summed.
The sums obtained above are combined into a value that is returned by the system. This process may be done by weighting each category by its truth sum and multiplying by the mean truth value of each category. The calculations involved may be more complex, depending on the difficulty of the fuzzy membership graphs.

12. What are Neural networks?

A neural network is a set of connected input/output units where each connection has a weight associated with it. During the knowledge phase, the network acquires by adjusting the weights to be able to predict the correct class label of the input samples. Neural network learning is also denoted as connectionist learning due to the connections between units. Neural networks involve long training times and are therefore more appropriate for applications where this is feasible. They require a number of parameters that are typically best determined empirically, such as the network topology or “structure”. Neural networks have been criticized for their poor interpretability since it is difficult for humans to take the symbolic meaning behind the learned weights. These features firstly made neural networks less desirable for data mining.

The advantages of neural networks, however, contain their high tolerance to noisy data as well as their ability to classify patterns on which they have not been trained. In addition, several algorithms have newly been developed for the extraction of rules from trained neural networks. These issues contribute to the usefulness of neural networks for classification in data mining. The most popular neural network algorithm is the backpropagation algorithm, proposed in the 1980s

13. How Backpropagation Network Works?

A Backpropagation learns by iteratively processing a set of training samples, comparing the network’s estimate for each sample with the actual known class label. For each training sample, weights are modified to minimize the mean squared error between the network’s prediction and the actual class. These changes are made in the “backward” direction, i.e., from the output layer, through each concealed layer down to the first hidden layer (hence the name backpropagation). Although it is not guaranteed, in general, the weights will finally converge, and the knowledge process stops.

14. What is a Genetic Algorithm?

Genetic algorithm is a part of evolutionary computing which is a rapidly growing area of artificial intelligence. The genetic algorithm is inspired by Darwin’s theory about evolution. Here the solution to a problem solved by the genetic algorithm is evolved. In a genetic algorithm, a population of strings (called chromosomes or the genotype of the gen me), which encode candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem, is evolved toward better solutions. Traditionally, solutions are represented in the form of binary strings, composed of 0s and 1s, the same way other encoding schemes can also be applied.

15. What is Classification Accuracy?

Classification accuracy or accuracy of the classifier is determined by the percentage of the test data set examples that are correctly classified. The classification accuracy of a classification tree = (1 – Generalization error).

16. Define Clustering in Data Mining?

Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them.

17. Write a difference between classification and clustering?[IMP]

18. What is Supervised and Unsupervised Learning?[TCS interview question]

Supervised learning, as the name indicates, has the presence of a supervisor as a teacher. Basically supervised learning is when we teach or train the machine using data that is well labeled. Which means some data is already tagged with the correct answer. After that, the machine is provided with a new set of examples(data) so that the supervised learning algorithm analyses the training data(set of training examples) and produces a correct outcome from labeled data.

Unsupervised learning is the training of a machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. Here the task of the machine is to group unsorted information according to similarities, patterns, and differences without any prior training of data.

Unlike supervised learning, no teacher is provided that means no training will be given to the machine. Therefore, the machine is restricted to find the hidden structure in unlabeled data by itself.

19. Name areas of applications of data mining?

Data Mining Applications for Finance
Intelligence
Telecommunication
Supermarkets
Crime Agencies
Businesses Benefit from data mining

20. What are the issues in data mining?

A number of issues that need to be addressed by any serious data mining package

Uncertainty Handling
Dealing with Missing Values
Dealing with Noisy data
Efficiency of algorithms
Constraining Knowledge Discovered to only Useful
Incorporating Domain Knowledge
Size and Complexity of Data
Data Selection
Understandably of Discovered Knowledge: Consistency between Data and Discovered Knowledge.

21. Give an introduction to data mining query language?

DBQL or Data Mining Query Language proposed by Han, Fu, Wang, et.al. This language works on the DBMiner data mining system. DBQL queries were based on SQL(Structured Query language). We can this language for databases and data warehouses as well. This query language support ad hoc and interactive data mining.

22. Differentiate Between Data Mining And Data Warehousing?

Data Mining: It is the process of finding patterns and correlations within large data sets to identify relationships between data. Data mining tools allow a business organization to predict customer behavior. Data mining tools are used to build risk models and detect fraud. Data mining is used in market analysis and management, fraud detection, corporate analysis, and risk management. It is a technology that aggregates structured data from one or more sources so that it can be compared and analyzed rather than transaction processing.

Data Warehouse : A data warehouse is designed to support the management decision-making process by providing a platform for data cleaning, data integration, and data consolidation. A data warehouse contains subject-oriented, integrated, time-variant, and non-volatile data.

Data warehouse consolidates data from many sources while ensuring data quality, consistency, and accuracy. Data warehouse improves system performance by separating analytics processing from transnational databases. Data flows into a data warehouse from the various databases. A data warehouse works by organizing data into a schema that describes the layout and type of data. Query tools analyze the data tables using schema.

23.What is Data Purging?

The term purging can be defined as Erase or Remove. In the context of data mining, data purging is the process of remove, unnecessary data from the database permanently and clean data to maintain its integrity.

24. What Are Cubes?

A data cube stores data in a summarized version which helps in a faster analysis of data. The data is stored in such a way that it allows reporting easily. E.g. using a data cube A user may want to analyze the weekly, monthly performance of an employee. Here, month and week could be considered as the dimensions of the cube.

25.What are the differences between OLAP And OLTP?[IMP]

26. Explain Association Algorithm In Data Mining?

Association analysis is the finding of association rules showing attribute-value conditions that occur frequently together in a given set of data. Association analysis is widely used for a market basket or transaction data analysis. Association rule mining is a significant and exceptionally dynamic area of data mining research. One method of association-based classification, called associative classification, consists of two steps. In the main step, association instructions are generated using a modified version of the standard association rule mining algorithm known as Apriori. The second step constructs a classifier based on the association rules discovered.

27. Explain how to work with data mining algorithms included in SQL server data mining?

SQL Server data mining offers Data Mining Add-ins for Office 2007 that permits finding the patterns and relationships of the information. This helps in an improved analysis. The Add-in called a Data Mining Client for Excel is utilized to initially prepare information, create models, manage, analyze, results.

28. Explain Over-fitting?

The concept of over-fitting is very important in data mining. It refers to the situation in which the induction algorithm generates a classifier that perfectly fits the training data but has lost the capability of generalizing to instances not presented during training. In other words, instead of learning, the classifier just memorizes the training instances. In the decision trees over fitting usually occurs when the tree has too many nodes relative to the amount of training data available. By increasing the number of nodes, the training error usually decreases while at some point the generalization error becomes worse. The Over-fitting can lead to difficulties when there is noise in the training data or when the number of the training datasets, the error of the fully built tree is zero, while the true error is likely to be bigger.

There are many disadvantages of an over-fitted decision tree:

Over-fitted models are incorrect.
Over-fitted decision trees require more space and more computational resources.
They require the collection of unnecessary features.

29. Define Tree Pruning?

When a decision tree is built, many of the branches will reflect anomalies in the training data due to noise or outliers. Tree pruning methods address this problem of over-fitting the data. So the tree pruning is a technique that removes the overfitting problem. Such methods typically use statistical measures to remove the least reliable branches, generally resulting in faster classification and an improvement in the ability of the tree to correctly classify independent test data. The pruning phase eliminates some of the lower branches and nodes to improve their performance. Processing the pruned tree to improve understandability.

30. What is a Sting?

Statistical Information Grid is called STING; it is a grid-based multi-resolution clustering strategy. In the STING strategy, every one of the items is contained into rectangular cells, these cells are kept into different degrees of resolutions and these levels are organized in a hierarchical structure.

31 . Define Chameleon Method?

Chameleon is another hierarchical clustering technique that utilization dynamic modeling. Chameleon is acquainted with recover the disadvantages of the CURE clustering technique. In this technique, two groups are combined, if the interconnectivity between two clusters is greater than the inter-connectivity between the object inside a cluster/ group.

32. Explain the Issues regarding Classification And Prediction?

Preparing the data for classification and prediction:

Data cleaning
Relevance analysis
Data transformation
Comparing classification methods
Predictive accuracy
Scalability
Interpretability

33.Explain the use of data mining queries or why data mining queries are more helpful?

The data mining queries are primarily applied to the model of new data to make single or multiple different outcomes. It also permits us to give input values. The query can retrieve information effectively if a particular pattern is defined correctly. It gets the training data statistical memory and gets the specific design and rule of the common case addressing a pattern in the model. It helps in extracting the regression formulas and other computations. It additionally recovers the insights concerning the individual cases utilized in the model. It incorporates the information which isn’t utilized in the analysis, it holds the model with the assistance of adding new data and perform the task and cross-verified.

34. What is a machine learning-based approach to data mining?

This question is the high-level Data Mining Interview Questions asked in an Interview. Machine learning is basically utilized in data mining since it covers automatic programmed processing systems, and it depended on logical or binary tasks. . Machine learning for the most part follows the rule that would permit us to manage more general information types, incorporating cases and in these sorts and number of attributes may differ. Machine learning is one of the famous procedures utilized for data mining and in Artificial intelligence too.

35.What is the K-means algorithm?

K-means clustering algorithm – It is the simplest unsupervised learning algorithm that solves clustering problems. K-means algorithm partition n observations into k clusters where each observation belongs to the cluster with the nearest mean serving as a prototype of the cluster.

Figure: K-Means Clustering division of attribute

36. What are precision and recall?[IMP]

Precision is the most commonly used error metric in the n classification mechanism. Its range is from 0 to 1, where 1 represents 100%.

Recall can be defined as the number of the Actual Positives in our model which has a class label as Positive (True Positive)”. Recall and the true positive rate is totally identical. Here’s the formula for it:

Recall = (True positive)/(True positive + False negative)

37. What are the ideal situations in which t-test or z-test can be used?

It is a standard practice that a t-test is utilized when there is an example size under 30 attributes and the z-test is viewed as when the example size exceeds 30 by and large.

38. What is the simple difference between standardized and unstandardized coefficients?

In the case of normalized coefficients, they are interpreted dependent on their standard deviation values. While the unstandardized coefficient is estimated depending on the real value present in the dataset.

39. How are outliers detected?

Numerous approaches can be utilized for distinguishing outliers anomalies, but the two most generally utilized techniques are as per the following:

Standard deviation strategy: Here, the value is considered as an outlier if the value is lower or higher than three standard deviations from the mean value.
Box plot technique: Here, a value is viewed as an outlier if it is lesser or higher than 1.5 times the interquartile range (IQR)

40. Why is KNN preferred when determining missing numbers in data?

K-Nearest Neighbour (KNN) is preferred here because of the fact that KNN can easily approximate the value to be determined based on the values closest to it.

The k-nearest neighbor (K-NN) classifier is taken into account as an example-based classifier, which means that the training documents are used for comparison instead of an exact class illustration, like the class profiles utilized by other classifiers. As such, there’s no real training section. once a new document has to be classified, the k most similar documents (neighbors) are found and if a large enough proportion of them are allotted to a precise class, the new document is also appointed to the present class, otherwise not. Additionally, finding the closest neighbors is quickened using traditional classification strategies.

41. Explain Prepruning and Post pruning approach in Classification?

Prepruning: In the prepruning approach, a tree is “pruned” by halting its construction early (e.g., by deciding not to further split or partition the subset of training samples at a given node). Upon halting, the node becomes a leaf. The leaf may hold the most frequent class among the subset samples, or the probability distribution of those samples. When constructing a tree, measures such as statistical significance, information gain, etc., can be used to assess the goodness of a split. If partitioning the samples at a node would result in a split that falls below a pre-specified threshold, then further partitioning of the given subset is halted. There are problems, however, in choosing a proper threshold. High thresholds could result in oversimplified trees, while low thresholds could result in very little simplification.

Postpruning: The postpruning approach removes branches from a “fully grown” tree. A tree node is pruned by removing its branches. The cost complexity pruning algorithm is an example of the post pruning approach. The pruned node becomes a leaf and is labeled by the most frequent class among its former branches. For every non-leaf node in the tree, the algorithm calculates the expected error rate that would occur if the subtree at that node were pruned. Next, the predictable error rate occurring if the node were not pruned is calculated using the error rates for each branch, collective by weighting according to the proportion of observations along each branch. If pruning the node leads to a greater probable error rate, then the subtree is reserved. Otherwise, it is pruned. After generating a set of progressively pruned trees, an independent test set is used to estimate the accuracy of each tree. The decision tree that minimizes the expected error rate is preferred.

42. How can one handle suspicious or missing data in a dataset while performing the analysis?

If there are any inconsistencies or uncertainty in the data set, a user can proceed to utilize any of the accompanying techniques: Creation of a validation report with insights regarding the data in conversation Escalating something very similar to an experienced Data Analyst to take a look at it and accept a call Replacing the invalid information with a comparing substantial and latest data information Using numerous methodologies together to discover missing values and utilizing approximation estimate if necessary.

43.What is the simple difference between Principal Component Analysis (PCA) and Factor Analysis (FA)?

Among numerous differences, the significant difference between PCA and FA is that factor analysis is utilized to determine and work with the variance between variables, but the point of PCA is to explain the covariance between the current segments or variables.

44 . What is the difference between Data Mining and Data Analysis?

45. What is the difference between Data Mining and Data Profiling?

Data Mining: Data Mining refers to the analysis of information regarding the discovery of relations that have not been found before. It mainly focuses on the recognition of strange records, conditions, and cluster examination.
Data Profiling : Data Profiling can be described as a process of analyzing single attributes of data. It mostly focuses on giving significant data on information attributes, for example, information type, recurrence, and so on.

46. What are the important steps in the data validation process?

As the name proposes Data Validation is the process of approving information. This progression principally has two methods associated with it. These are Data Screening and Data Verification.

Data Screening : Different kinds of calculations are utilized in this progression to screen the whole information to discover any inaccurate qualities.
Data Verification: Each and every presumed value is assessed on different use-cases, and afterward a final conclusion is taken on whether the value must be remembered for the information or not.

47. What is the difference between univariate, bivariate , and multivariate analysis?

The main difference between univariate, bivariate, and multivariate investigation are as per the following:

Univariate : A statistical procedure that can be separated depending on the check of factors required at a given instance of time.
Bivariate : This analysis is utilized to discover the distinction between two variables at a time.
Multivariate : The analysis of multiple variables is known as multivariate. This analysis is utilized to comprehend the impact of factors on the responses.

48. What is the difference between variance and covariance?

Variance and Covariance are two mathematical terms that are frequently in the Statistics field. Variance fundamentally processes how separated numbers are according to the mean. Covariance refers to how two random/irregular factors will change together. This is essentially used to compute the correlation between variables.

49. What are different types of Hypothesis Testing?

The various kinds of hypothesis testing are as per the following:

T-test : A T-test is utilized when the standard deviation is unknown and the sample size is nearly small.
Chi-Square Test for Independence: These tests are utilized to discover the significance of the association between all categorical variables in the population sample.
Analysis of Variance (ANOVA): This type of hypothesis testing is utilized to examine contrasts between the methods in different clusters. This test is utilized comparatively to a T-test but, is utilized for multiple groups.

Welch’s T-test: This test is utilized to discover the test for equality of means between two testing sample tests.

50. Why should we use data warehousing and how can you extract data for analysis?

Data warehousing is a key technology on the way to establishing business intelligence. A data warehouse is a collection of data extracted from the operational or transactional systems in a business, transformed to clean up any inconsistencies in identification coding and definition, and then arranged to support rapid reporting and analysis.

Here are some of the benefits of a data warehouse:

It is separate from the operational database.
Integrates data from heterogeneous systems.
Storage a huge amount of data, more historical than current data.
Does not require data to be highly accurate.

Bonus Interview Questions & Answers

1. What is Visualization?

Visualization is for the depiction of data and to gain intuition about the data being observed. It assists the analysts in selecting display formats, viewer perspectives, and data representation schema.

2. Give some data mining tools?

Multimedia miner
WeblogMiner

3. What are the most significant advantages of Data Mining?

There are many advantages of Data Mining. Some of them are listed below:

Data Mining is used to polish the raw data and make us able to explore, identify, and understand the patterns hidden within the data.
It automates finding predictive information in large databases, thereby helping to identify the previously hidden patterns promptly.
It assists faster and better decision-making, which later helps businesses take necessary actions to increase revenue and lower operational costs.
It is also used to help data screening and validating to understand where it is coming from.
Using the Data Mining techniques, the experts can manage applications in various areas such as Market Analysis, Production Control, Sports, Fraud Detection, Astrology, etc.
The shopping websites use Data Mining to define a shopping pattern and design or select the products for better revenue generation.
Data Mining also helps in data optimization.
Data Mining can also be used to determine hidden profitability.

4. What are ‘Training set’ and ‘Test set’?

In various areas of information science like machine learning, a set of data is used to discover the potentially predictive relationship known as ‘Training Set’. The training set is an example given to the learner, while the Test set is used to test the accuracy of the hypotheses generated by the learner, and it is the set of examples held back from the learner. The training set is distinct from the Test set.

5. Explain what is the function of ‘Unsupervised Learning?

Find clusters of the data
Find low-dimensional representations of the data
Find interesting directions in data
Interesting coordinates and correlations
Find novel observations/ database cleaning

6. In what areas Pattern Recognition is used?

Pattern Recognition can be used in

Computer Vision
Speech Recognition
Data Mining
Informal Retrieval
Bio-Informatics

7. What is ensemble learning?

To solve a particular computational program, multiple models such as classifiers or experts are strategically generated and combined to solve a particular computational program Multiple. This process is known as ensemble learning. Ensemble learning is used when we build component classifiers that are more accurate and independent of each other. This learning is used to improve classification, prediction of data, and function approximation.

8. What is the general principle of an ensemble method and what is bagging and boosting in the ensemble method?

The general principle of an ensemble method is to combine the predictions of several models built with a given learning algorithm to improve robustness over a single model. Bagging is a method in an ensemble for improving unstable estimation or classification schemes. While boosting methods are used sequentially to reduce the bias of the combined model. Boosting and Bagging both can reduce errors by reducing the variance term.

9. What are the components of relational evaluation techniques?

The important components of relational evaluation techniques are

Data Acquisition
Ground Truth Acquisition
Cross-Validation Technique
Scoring Metric
Significance Test

10. What are the different methods for Sequential Supervised Learning?

The different methods to solve Sequential Supervised Learning problems are

Sliding-window methods
Recurrent sliding windows
Hidden Markov models
Maximum entropy Markov models
Conditional random fields
Graph transformer networks

11. What is a Random Forest?

Random forest is a machine learning method that helps you to perform all types of regression and classification tasks. It is also used for treating missing values and outlier values.

12. What is reinforcement learning?

Reinforcement Learning is a learning mechanism about how to map situations to actions. The end result should help you to increase the binary reward signal. In this method, a learner is not told which action to take but instead must discover which action offers a maximum reward. This method is based on the reward/penalty mechanism.

13. Is it possible to capture the correlation between continuous and categorical variables?

Yes, we can use the analysis of the covariance technique to capture the association between continuous and categorical variables.

14. What is Visualization?

Visualization is for the depiction of information and to acquire knowledge about the information being observed. It helps the experts in choosing format designs, viewer perspectives, and information representation patterns.

15. Name some best tools which can be used for data analysis.

The most common useful tools for data analysis are:

Google Search Operators

16. Describe the structure of Artificial Neural Networks?

An artificial neural network (ANN) also referred to as simply a “Neural Network” (NN), could be a process model supported by biological neural networks. Its structure consists of an interconnected collection of artificial neurons. An artificial neural network is an adjective system that changes its structure-supported information that flows through the artificial network during a learning section. The ANN relies on the principle of learning by example. There are, however, 2 classical types of neural networks, perceptron and also multilayer perceptron. Here we are going to target the perceptron algorithmic rule.

17. Do you think 50 small decision trees are better than a large one? Why?

Yes,50 small decision trees are better than a large one because 50 trees make a more robust model (less subject to over-fitting) and simpler to interpret.

Please Login to comment...

Improve your Coding Skills with Practice

What kind of Experience do you want to share?

Data Mining: A Critical Discussion Analytical Essay

Introduction, reference list.

In recent times, the relatively new discipline of data mining has been a subject of widely published debate in mainstream forums and academic discourses, not only due to the fact that it forms a critical constituent in the more general process of Knowledge Discovery in Databases (KDD), but also due to the increased realization that this discipline can be applied in a number of areas to enhance decision making processes, efficiency, and competitiveness in contemporary organizations (Kusiak, 2006).

The basic concept behind the emergence of data mining, and which has contributed immensely to its admissibility as one of the increasingly used strategies in business establishments as well as scientific and research undertakings, is that by automatically sifting through large volumes of information which may primarily appear irrelevant, it should be possible for interested parties to extract nuggets of useful knowledge which can then be used to drive their agenda forward (Adams, 2010).

Goth (2010) observes that the emergence of data mining has been primarily informed by the rapid growth in data warehouses as well as the recognition that this heap of operational data can be potentially exploited as an extension of both business and scientific intelligence.

The present paper seeks to critically discuss the discipline of data mining with a view to illuminate knowledge about its origins, concepts, applications, and the legal and ethical issues involved in this particular field.

Definition & History of Data Mining

Although data mining as a concept has been defined differentially in diverse mediums, this report will adopt the simple definition given by Payne & Trumbach (2009), that “…data mining is the set of activities used to find new, hidden or unexpected patterns in data” (p. 241-242).

The purpose of data mining, as observed by these authors, is to extract information that would not be readily established by searching databases of raw data alone. Through data mining, organizations are now able to combine data from incongruent sources, both internal and external, from across a multiplicity of platforms with a view to assist in a variety of business applications.

At its most elemental state, data mining utilizes proved procedures, including modeling techniques, statistical investigation, machine learning, and database technology, among others, to seek prototypes of data and fine relationships in the sifted data with the main objective of deducing rules and intricate relationships that will inarguably permit the extrapolation of future outcomes (Pain & Trumbach, 2009; Adams, 2010).

Researchers and practitioners are in agreement that the capability of both generating and collecting data from a wide variety of sources has greatly impacted the growth trajectories of data mining as a discipline.

This capability, according to Adams (2010) and Chen (2006), was precipitated by a number of variables, which can be categorized into the following:

increased computerization of business, scientific, and government transactions with the view to increase efficiency and productivity,
extensive usage of electronic cameras, scanners, publication devices, and internationally recognized bar codes for most business-related products,
advances in data gathering instruments ranging from scanned documents and image platforms to global positioning and remote sensing systems,
the development and popularization of the World Wide Web and the internet as widely accepted global information systems.

This explosive growth in stored or ephemeral data brought us to the information age, which was, and continues to be, characterized by an imperative need to develop new techniques, procedures and automated tools that can astutely assist us in transforming and making sense of the huge quantities of data collected via the above stated protocols (Goth, 2010).

To dig a bit deeper into the history of data mining, research has been able to establish that the term ‘data mining’, which was introduced in the 1990s, has its origins in three interrelated family lines. It is important to note that the convergence of these family lines to develop a unique discipline in the context of data mining certainly gives it its scientific foundation (Adams, 2010).

This notwithstanding, extant research (Adams, 2010; Chez, 2006) demonstrate that the longest of these family lines to be credited with the gradual development of data mining as a fully-fledged discipline is known as classical statistics.

Researchers are in agreement that it would not have been possible to develop the field of data mining in the absence of statistics as the latter provides the foundation of most technologies on which the former is built, such as “regression analysis, standard distribution, standard deviation, standard variance, discriminant analysis, and confidence intervals” (Goth, 2010, p. 14).

All these concepts, according to this author, are used to study data and data relationships – central aspects in any data mining exercise.

The second longest family line that has contributed immensely to the emergence of data mining as a fully-fledged field is known as artificial intelligence, or simply AI. Extant research demonstrate that the AI discipline, which is developed upon heuristics as opposed to statistics, endeavors to apply human-thought-like processing to statistical challenges while using computer processing power as the appropriate medium (Talia & Trunfio, 2010).

It is important to mention that since this approach was tied to the availability of computers and supercomputers to undertake the heuristics, it was not practical until the early 1980s, when computers started trickling into the market at reasonable prices (Goth, 2010).

The third family line to have influenced the field of data mining is what is generally known as machine learning or, better still, the amalgamation of statistics and AI (Adams, 2010). Here, it is of importance to note that while AI could not have been viewed as a commercial success during the formative years, its techniques and strategies were largely co-opted by machine learning.

It is also important to note that machine learning, while able to take the full benefit of the ever-improving price/performance quotients provided by computers in the decades of the 1980s and 1990s, found usage in more applications because the entry price was lower that that of AI, not mentioning that it was largely considered as an evolved facet of AI as it was effectively able to blend AI heuristics with complex statistical analysis (Chen, 2006).

Review of how Data Mining is used Today and how it could be used in the Future

Presently, there exist broad consensus that data mining is mostly based on the machine leaning techniques; that is, it is fundamentally perceived as the adaptation of machine learning techniques and concepts to a wide variety of areas, such as business and scientific applications (Adams, 2010).

Therefore, the present-day data mining can only be described as the amalgamation of historical and recent developments, particularly in statistics, artificial intelligence, and machine learning, with a view to developing a software program that can run on a standard computer to, among other things, make diverse decisions based on the data under study, use statistical concepts and applications to establish various relationships among the data, and also use more advanced artificial intelligence heuristics and algorithms to achieve its major goal (Talia & Trunfio, 2010).

Extant research demonstrate that the major objective of current data mining applications is to sift through huge volumes of data to extract nuggets of useful data, which can then be used to establish previously-hidden trends or patterns.

Today, more than ever before, data mining is used in the business arena to boost corporate profits by improving customer relations and targeting new customers (Cary et al, 2003).

According to these authors, “…AT&T Wireless was able to increase it’s subscriber base by 20% in less than a year when it contracted with a data-mining company to identify customers that would likely to be interested in AT&T’s new flat-fee wireless service” (p. 158).

The AT&T story demonstrates that visions of achieving good returns continue to drive businesses toward embracing data mining technology.

Data mining is bound to be used along the same lines in the future to enable enterprises make critical decisions from a knowledge-oriented perspective. Consecutive studies have demonstrated that most business organizations fail to wade through the harsh economic waters of modern times due to their perceived inadequacy to base their most important decisions on knowledge and evidence (Adams, 2010; Goth, 2010).

However, it is now evident that data mining can be used to endear organizations closer to a knowledge-based economy, which basically translates into the use of knowledge to generate economic benefits.

Chen (2006) observes that a knowledge-based economy necessitates data mining processes to become more goal-oriented with the view to generating an enabling environment where more tangible results can be achieved.

Consequently, data mining should be used in the future not only to facilitate the uncovering of concealed knowledge beneath the ocean of data readily found in a multiplicity of mediums and applications, but also to ensure that it makes important contributions to the knowledge-based economy with the express intention of coming up with more tangible business and scientific outcomes (Chen, 2006; Adams, 2010).

Types of Data Mining Applications

There exist a multiplicity of data mining applications which can be used in diverse situations and environments depending on the major objective for usage. Some data mining applications, according to Chen (2006), are simple to use and may be offered for free, while others are complex and require a sizeable investment to operationalize.

This section will discuss some data mining applications based on the sector of practice, and will mainly focus attention to the banking and finance, retail, and the healthcare sectors of the economy.

Data Mining Applications in the Banking & Finance Sector

Most banking institutions have over the years employed a multiplicity of data mining applications to model and predict credit fraud, to assess borrower risk, to undertake trend analysis, and to evaluate profitability, as well as to assist in the initiation and management of direct marketing activities (Seifert, 2004).

In equal measure, most finance and credit companies have over the years employed a variety of neural networks and other data mining applications “…in stock-price forecasting, in option trading, in bond rating, in portfolio management, in commodity price prediction, in mergers and acquisitions, as well as in forecasting financial disasters” (p. 191).

Here, it can be noted that the Neural Applications Corporation has developed an effective application known as NETPROPHET, which is increasingly being used by finance companies to make stock predictions by illustrating the real and predicted stock values depending on the type of data that has been keyed into the system (Groth, 1999; Chen, 2006).

The banking sector is continuously been faced with fraud cases, and data mining applications such as HNC Falcon has assisted the institutions to monitor payment-card applications, decreasing fraud cases by almost 75 percent while increasing applications for payment card accounts by as much as 50 percent on a yearly basis (Groth, 1999).

The importance of banks to develop data mining applications that could be used in cross-selling and maintenance of customer loyalty has been well documented in literature.

These applications, according to Groth (1999), mainly assist banking institutions to model the behavior of their customers in such a manner that the resulting relationships could be used to establish the needs and demands of their customers, as well as make objective predictions into the future.

RightPoint software, Security First, and BroadVision are some of the vendors primarily interested in integrating predictive technologies with consumer interaction points to ensure customer needs and demands are efficiently dealt with (Groth, 1999; Chen, 2006), in addition to using predictive technologies to integrate one-to-one marketing strategies to their clients banking sites (Adam, 2010).

According to Groth (1999), “…the RightPoint Real-Time Marketing Suite takes data-mining models and leverages them within real-time interactions with customers” (p. 194). This application is unique in that it is designed to develop, manage and deliver one-to-one marketing initiatives for high-end industries that heavily depend on direct customer interaction to undertake business (Goth, 2010).

As a general prerogative, it is important to note that majority of the data mining applications used in the banking and finance sector attempt to ensure that each customer interaction seizes the prospect of enhancing customer satisfaction, loyalty, motivation, and profit-generation potential (Talia & Trunfio, 2010; Zhang & Segall, 2008).

Data mining Applications in Retail

Intense competition and slim profit margins have obliged retailers to embrace data warehousing strategies earlier than other sectors. As observed by Groth (1999), “…retailers have seen improved decision-support processes lead directly to improved efficiency in inventory management and financial forecasting” (p. 198).

It is a well known fact that expansive retail and supermarket chains are in possession of huge quantities of point-of-sale data that is not only information-rich, but could be employed using appropriate data mining applications to improve the stated decision-support strategies, improve efficiency in financial predictions and inventory management, and analyze customer shopping patterns (Seifert, 2004).

In the retail sector, the AREAS Property Valuation product from HNC software, as well as SABRE Decision Technologies, serves as good examples on how data mining applications can be used in the retail sector to perform valuations, projection and forecasting, customer purchasing behavior analysis, and customer retention analysis, with the underlying purpose of increasing profitability, enhancing customer experience, and making better and more informed business decisions (Zhang & Segall, 2008).

In evaluating customer profitability in the retail sector, a software vendor referred to as Dovetail Solutions has developed a data mining application known as Value, Activity, and Loyalty TM (VAL TM ), with a view to utilize transactional business data from the retailers to synthesize information about customer activity and processes, churn rate, and anticipated future purchases (Groth, 1999; Chen, 2006).

Data Mining Applications in the Medical Field

The vast amount of data available within the healthcare industry, including the associated data collected via medical research, biotechs, and the pharmaceutical industry, have provided a fertile ground for data mining applications to grow. The knowledge that data mining has been employed expansively in the medical industry is in the public domain.

For example, we are aware that the vendor NeuroMedical Systems ingeniously employed neural networks to create a pap smear diagnostic aid, while both Vysis Rochester Cancer Center and the Oxford Transplant Center continues to employ a data mining application known as KnowledgeSEEKER, which utilizes a decision tree technology, to assist in various research undertakings (Groth, 1999; Adams, 2010; Chen, 2006).

It is important to note that these applications are beneficial in the medical sector as they enable health practitioners to come up with accurate diagnosis even without subjecting patients to physical examination (Koh & Tan, 2008).

Governments and other interested health agencies can utilize data mining applications, such as MapInfo, KnowledgeSEEKER, and LEADERS, among others, to: demonstrate average costs of health services; show efficiency of a particular prescription over time; reveal efficacy rates of diverse pathogens over time; develop superior diagnosis and treatment protocols; show patient location in order to deliver superior health services; or assist healthcare insurers to detect fraud (Koh & Tan, 2008; Wen-Chung et al, 2010).

Legal & Ethical Issues in Data Mining

As is the case in other disciplines, the field of data mining is faced with a complexity of legal and ethical issues which needs to be addressed for the applications to succeed.

In the legal arena, it is important to evaluate how organizations should employ data mining applications while remaining focused on protecting the private information of their customers so as to avoid customer dissatisfaction or even being subjected to legal action by customers who may feel that the organizations intruded into their privacy (Cary et al, 2003; Wen-Chung et al, 2010).

In terms of ethical issues, it is a well known fact the spread of personal information, as is the case in many data mining applications, can lead to elevated risks of customer identity theft (Cary et al, 2003).

According to Payne & Trumbach (2009), data mining processes brings into the fore a scenario where “…the consumer loses aspects of privacy as all of their basic demographic information, personal interests, correspondence and activities are stored in databases and available to be combined together” (p. 243).

Such a scenario has obvious ethical ramifications since this information can be used to the disadvantage of the customers.

Another ethical query arises from the fact that consumers lose the control over what happens to their personal information held in large databases, implying that such kind of information can be used to the disadvantage of the providers if it happens to fall into the wrong hands (Payne & Trumbach, 2009; Cary et al 2003; McGraw, 2010).

What’s more, customers who provide personal information to organizations face a more ominous challenge that may entail potential discrimination based on the personal information they either provide or refuse to provide to the organizations.

Another important factor to consider when evaluating ethical concerns in data mining is that there is no fine line distinguishing if it is indeed necessary for an organization to use the private information of its customers to enhance its profitability or if such information should be sorely used to improve customer satisfaction and maintain consumer trust (Payne & Trumbach, 2009).

Lastly, it is well known that a number of data mining processes may yield incorrect conclusions, which may be costly to the organization as well as to the customers (McGraw, 2010).

This discussion has brought into the fore important aspects of data mining, its current and future uses, as well as perceived limitations in terms of legal and ethical constraints.

The general consensus among academics and practitioners is that data mining represents the new frontier of growth, particularly in nurturing mutually fulfilling customer relationships, ensuring that customer’s needs and demands are satisfactorily met, and in facilitating organizations to forecast and predict future growth and decision patterns (Adams, 2010; Kusiak, 2006; Wen-Chung et al, 2010).

The task is therefore for developers to continue investing heavily in effective and efficient data mining applications to ensure that such tools are able to achieve what they were originally intended to achieve. Consequently, research and development into these applications and tools is of primary importance.

Adams, N.M. (2010). Perspectives on data mining. International Journal of Market Research, 52 (1), 11-19. Retrieved from Business Source Premier Database

Cary, C., Wen, H.J., & Mahatanankoon, P. (2003). Data mining: Consumer privacy, ethical policy, and systems development practices. Human Systems Management, 22 (4), 157-168. Retrieved from Business Source Premier Database

Chen, Z. (2006). From data mining to behavior mining. International Journal of Information Technology & Decision Making, 5 (4), 703-711. Retrieved from Business Source Premier Database

Goth, G. (2010). Turning data into knowledge. Communications of the ACM, 53 (11), 13-15. Retrieved from Business Source Premier Database

Groth, R. (1999). Data mining: Building competitive advantage . Upper Saddle River, NJ: Prentice Hall

Koh, H.C., & Tan, G. (2008). Data mining applications in healthcare. Journal of Healthcare Information Management, 19 (2), 64-72

Kusiak, A. (2006). Data mining: Manufacturing and service applications. International Journal of Production Research, 44 (18/19), 4175-4191. Retrieved from Business Source Premier Database

McGraw, D. (2010). Data identifiability and privacy. American Journal of Bioethics, 10 (9), 30-31. Retrieved from Academic Search Premier Database

Payne, D., Trumbach, C.C. (2009). Data mining: Proprietary rights, people and proposals. Business Ethics: A European Review, 18 (3), 241-252. Retrieved from Business Source Premier Database

Seifert, J.W. (2004). Data mining: An overview . Retrieved from < https://fas.org/irp/crs/RL31798.pdf >

Talia, D., & Trunfio, P. (2010). How distributed data mining tasks can thrive as knowledge services. Communications of the ACM, 53 (7), 132-137. Retrieved from Business Source Premier Database

Wen-Chung, S., Chao-Tung, Y., & Shian-Shyong, T. (2010). Performance-based data distribution for data mining applications on grid computing environments. Journal of Supercomputing, 52 (2), 171-198. Retrieved from Academic Search Premier Database

Zhang, Q., & Segall, R.S. (2008). Web mining: A survey of current research, techniques, and software. International Journal of Information Technology & Decision Making, 7 (4), 683-720. Retrieved from Business Source Premier Database

Chicago (A-D)
Chicago (N-B)

IvyPanda. (2023, December 3). Data Mining: A Critical Discussion. https://ivypanda.com/essays/data-mining-a-critical-discussion/

"Data Mining: A Critical Discussion." IvyPanda , 3 Dec. 2023, ivypanda.com/essays/data-mining-a-critical-discussion/.

IvyPanda . (2023) 'Data Mining: A Critical Discussion'. 3 December.

IvyPanda . 2023. "Data Mining: A Critical Discussion." December 3, 2023. https://ivypanda.com/essays/data-mining-a-critical-discussion/.

1. IvyPanda . "Data Mining: A Critical Discussion." December 3, 2023. https://ivypanda.com/essays/data-mining-a-critical-discussion/.

Bibliography

IvyPanda . "Data Mining: A Critical Discussion." December 3, 2023. https://ivypanda.com/essays/data-mining-a-critical-discussion/.

“Data Mining and Customer Relationship Marketing in the Banking Industry“ by Chye & Gerry
Issues With Data Mining
Copper Mining in Economy, Environment, Society
Insight Art: Achieving Goals in Strategic Management
Time Management and Building Team
Sales Planning, Telemarketing and Customer Relationship Management
Effective Management in International Business: Lessons from Four Seasons’ Expansion to France
Improvement, Integration and Innovation

Home / Essay Samples / Information Science and Technology / Computer Science / Data Mining

Data Mining Essay Examples

Detection of outlier: data mining.

In data mining reprocessing means preparing data. It is the one of the important and compulsory task. Before applying the data mining technique like association, classification, or clustering noisy and outlier should be removed. We have proposed replicator neural network (runs) as on outlier detecting algorithm....

My Desire to Become a Part of the Hot Field of Data Science

The omnipresence of technology, from digital communications to underwater wireless transmissions, is a testament to the fact that the voluminous amount of data gets generated every second. This data is a driving as well as a defining force behind an infinite number of crucial decisions...

Cognitive Computing in Medical Field with Applications

This paper presents the importance of Cognitive Computing in real life and some important applications of Cognitive Computing in Medical Field. Keywords—cognitive computing, data mining, natural language processing, I To Cognitive computing Cognitive Computing is one of the most researched topic of 21st century. It...

Application of Data Mining Techniques in Pharmacovigilance

To discuss the potential use of data mining and knowledge discovery in databases for detection of adverse drug events (ADE) in pharmacovigilance. A literature search was conducted to identify articles, which contained details of data mining, signal generation or knowledge discovery in relation to adverse...

Application of Data Mining Techniques in the Analysis of Fire Incidents

In recent years, the number of fire accidents decreased slowly, but the losses due to fire accident is still enormous every year. Under these circumstances, with the combination of data mining, data analysis and accident analysis theory, Microsoft SQL Server business intelligence components was used...

Development of Data Mining Applications in Banking Sector

Based on the review of over 100 journals the authors concluded that applying different data mining technique for managing risk, fraud detection and CRM in the banking sector has been successful though there has been exception on big data because of different variable features. The...

Value Creation Using Big Data Mining in Banking

The banking sector deals with an overwhelming amount of data on a day to day basis. Banks have historical records and customer information data dating back to decades. Manually leveraging such enormous amounts of data for making decisions is not only time consuming but involves...

The Role of Artificial Intelligence in Improvement of the Data Mining and Predictive Analytics in Hospitals and Healthcare

Artificial Intelligence is everywhere in our lives before we realize it. It there when you are watching a video on youtube, browsing products on Amazon, or writing some words on google searching box. “AI assistants will become trusted advisers to consumers, anticipating and satisfying their...

Evaluation of Portugese Students' Progress in the Schools of Secondary Education Using Business Intelligence and Data Mining Techniques

Despite of the education levels in the Portuguese population has success in improving in the last decades, according to statistics keep Portugal at the tail of the European continent that's due to the very high student failures, especially the classes of lack of success are...

Television Program Popularity Analysis Using Data Mining Techniques

TV program prominence examination utilizing information mining strategies is a standout amongst the most fascinating and testing errands. A basic interest along this line is to anticipate the fame of online serials, which can empower an extensive variety of uses, for example, web based publicizing...

Trying to find an excellent essay sample but no results?

Don’t waste your time and get a professional writer to help!