research paper on data mining by ieee

IEEE ICDM 2022

22nd ieee international conference on data mining, nov. 28 – dec. 1, 2022 orlando, fl, usa, the world’s premier research, conference in data mining.

The IEEE International Conference on Data Mining (ICDM) has established itself as the world’s premier research conference in data mining. It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative and practical development experiences. The conference covers all aspects of data mining, including algorithms, software, systems, and applications. ICDM draws researchers, application developers, and practitioners from a wide range of data mining related areas such as big data, deep learning, pattern recognition, statistical and machine learning，databases, data warehousing, data visualization, knowledge-based systems, and high-performance computing. By promoting novel, high-quality research findings, and innovative solutions to challenging data mining problems, the conference seeks to advance the state-of-the-art in data mining.

Topics of interest

Topics of interest include, but are not limited to:

• Foundations, algorithms, models and theory of data mining, including big data mining.
• Deep learning and statistical methods for data mining.
• Mining from heterogeneous data sources, including text, semi-structured, spatio-temporal, streaming, graph, web, and multimedia data.
• Data mining systems and platforms, and their efficiency, scalability, security and privacy.
• Data mining for modelling, visualization, personalization, and recommendation.
• Data mining for cyber-physical systems and complex, time-evolving networks.
• Applications of data mining in social sciences, physical sciences, engineering, life sciences, web, marketing, finance, precision medicine, health informatics, and other domains.

We particularly encourage submissions in emerging topics of high importance such as ethical data analytics, automated data analytics, data-driven reasoning, interpretable modeling, modeling with evolving environment, cyber-physical systems, multi-modality data mining, and heterogeneous data integration and mining.

Data Mining

Featured article.

Guide to the top data mining algorithms

In today’s ever-expanding technological environment, companies—for instance, in banking, retail, and social media—store large batches of data online and across many systems. Companies can make use of this data and benefit from it through data mining . This article explores how data mining algorithms work and how you can use them. It also looks at some of the top data mining algorithms available today.

To start, data mining is an important step in the larger process of knowledge discovery. It is the process of exploring and analyzing large data sets for patterns, relationships, and trends. Companies engage in data mining to gain useful business insights. For example, a company might use data mining to analyze a group’s buying habits, bank transactions, or medical history to predict the group’s future actions.

People sometimes confuse data mining with data harvesting . However, data harvesting is the process of extracting and analyzing data from online sources. Data mining does not involve “harvesting” data. Instead, it centers on examining data to produce new information.

To do so, data mining typically uses a machine learning method called supervised learning . Supervised learning “teaches” algorithms new processes in data review and analysis. Typically, a supervised learning algorithm views data, applies conditions, acts on the data, and produces results. It then applies the same process and reasoning to new data.

What is an algorithm in data mining?

In general, algorithms employ a series of steps or rules to process data and produce a specific outcome, result, or prediction. Within data mining, algorithms perform functions such as analyzing, classifying, and forecasting data and monitoring data trends .

Data mining algorithms are types of supervised learning algorithms. They use learning algorithm elements like statistics, probability, and artificial intelligence to explore and generate results that benefit companies, industries, and organizations all over the world.

Supervised learning algorithms and other supervised learning methods depend on labeled data. The labeled data includes the algorithm’s expected data output. A simple example is a picture of a dog labeled with the word “dog.” Labeled data helps the algorithm “learn” patterns in the data and later apply these patterns to unlabeled sets of data. In the above example, a labeled picture of a dog could help an algorithm recognize other images of dogs.

Despite their use of labeled data, supervised learning algorithms can predict or estimate unknown data quantities in the future. This is possible as long as the calculations are based on prior patterns in known data.

What is the role of the algorithm in data mining?

Data mining algorithms process large groups of data to produce certain statistical analyses or results for businesses, industries, or organizations. As such, they are a vital part of the data mining process.

A data mining algorithm’s role depends on the expectations of a user, creator, or investor. As we noted previously, many data mining algorithms conduct data analysis on large data sets. Across many fields, data mining algorithms can analyze audio, textual, and visual data according to demographic factors such as age, gender, and income.

For instance, a shoe company might develop a data mining algorithm to uncover the percentage of the company’s stock that women between the ages of twenty-five and thirty own. An organization within the medical field might use data mining algorithms to conduct research on certain diseases and their impacts on different groups of patients. A social media company might use a data mining algorithm to provide facial tagging suggestions.

All of these examples rely on data points, or criteria, that work with a data mining algorithm to produce the best or closest desired outcome.

Many types of data mining algorithms exist to analyze and interpret data and help achieve desired results. Examples include decision trees , support vector machines, k-nearest neighbors, and neural networks. We will discuss these in more depth later.

However, despite this variety in data mining algorithms, the basic underlying process for all of them is similar. Regardless of its specific purpose, a data mining algorithm’s process—taking data and producing a result—remains the same.

What are the main components of an algorithm for data mining?

At a basic level, data mining algorithms contain different elements that, in combination, lead to a result. The main components of data mining algorithms include data, conditions, and expectations (i.e., end goals).

As we discussed previously, a data mining algorithm relies on data to operate. This data usually comes in the form of large data sets that the algorithm reviews and breaks down into smaller data sets. The algorithm breaks down and analyzes data in relationship to variables. Examples of variables include age, gender, salary, and location.

Different types of variables produce different results. Three types of variables that algorithms use are discrete, continuous, and categorical.

Discrete variables consist of finite (countable) numbers. An example is the number of people who attended a concert or the length of a piece of equipment. Continuous variables, in contrast, have an infinite number of values. An example is the date and time a company receives a payment. Categorical variables contain a finite number of categories or groups. These variables aren’t dependent on order. Examples include material type and payment method.

In an algorithm, multiple variables work together to create conditions. When the algorithm applies certain conditions, it produces specific results.

For example, say a company wants to see how many elderly customers buy a certain type of toothpaste. Conditions of the data mining algorithm would include variables such as customer age, toothpaste type, and purchase confirmation. When the algorithm applies these conditions, it can generate the company’s desired result.

Many data mining algorithms also use conditional probability to generate outcomes. Conditional probability involves events and if/then instances. We can look at a coin toss to illustrate this. If I toss a coin, then it will land as either heads or tails. The algorithm “learns” how to understand and use this conditional “language” in order to seek out a specific outcome. Through such language, for example, an algorithm can identify the face of a specific person in a database.

Often, data mining algorithms incorporate Bayes’s theorem of conditional probability and predictive analysis into their data mining processes. Bayes’s eighteenth-century theorem hinges on the fact that one event will likely happen because another event has already happened. Although the theorem is dated, its if/then concepts and approaches remain useful to determine outcomes today. Likewise, predictive analytics offer another way to estimate future impacts on other sets of large data.

How do you write an algorithm for data mining?

Data experts and programmers create data mining algorithms through careful thought, planning, and execution. By establishing input variables, conditions, and output variables, they create algorithms that produce models from data. These models can then predict future data outcomes based on past incidences.

It is important to keep in mind that data programmers write different types of algorithms to create data mining models with specific end goals in mind. Examples of data mining models include the following:

• A classification model to label loan applicants as low, medium, or high credit risk
• A decision tree to predict whether a particular consumer will like a product and describe how factors like age and gender will determine product popularity
• A mathematical model to forecast product sales
• A set of rules to explain the probabilities that a consumer will purchase a group of products together

Classifications are ways of breaking down and comparing data points. For example, the solar system breaks down into classifications such as planets, moons, and stars. If an algorithm tried to label a specific object in our solar system, it would likely consider these different classifications and their connections to each other in its analytical process.

Decision trees resemble classification models. They start with a main idea that breaks down into several other related ideas when an algorithm applies certain factors. In turn, those ideas break down further as the algorithm applies more conditions. Eventually, these “branches” of ideas lead to an end result.

Both human and machine learning use decision trees as part of the decision-making process. Decision trees present data simply and linearly. For this reason, they represent a key approach to data mining.

Mathematical processes are key to identifying correlations in large data sets and then creating predictions. Linear algebra and probability, for example, play an important role in some data mining models.

Rules are also important in data mining models. These rules tell a data mining algorithm where it should act first. Consider, for example, a situation in which you need to know the probability of event A to predict the likelihood that event B will happen because of event A. An important rule in the equation would instruct the algorithm to discern event A’s probability before proceeding to any other calculations.

Once operating, many data mining algorithms work independently, without human supervision. That’s what makes them part of the machine learning family. However, someone must first set up the algorithm and make adjustments as necessary. This is why we categorize data mining algorithms as supervised learning algorithms.

How to use data mining algorithms

Various industries use data mining algorithms for research, investigation, and analytical purposes. These algorithms produce useful insights from the large data sets that companies have at their disposal.

An example of a field employing data mining algorithms for research today is the medical field. Often, doctors and other medical professionals use different data mining algorithms to predict the prevalence of certain diseases, such as heart disease, among a population.

In contrast, law enforcement agencies and social media companies might use data mining algorithms for investigation and analytical purposes. Although for different reasons, both types of organizations might conduct facial recognition searches to confirm a person’s identity.

What should you look for in an algorithm for data mining?

It is important to choose a data mining algorithm that meets your specific needs and goals. As we have discussed above, data mining algorithms vary according to their purpose. If you are considering data mining, you want to ensure that you choose algorithms that fit with your intended purpose.

The ultimate goal of data mining is actionable insight. Finding patterns among large data sets alone might be interesting to an individual or company. But the true value of a data mining algorithm comes from the user’s ability to act on the new information that data mining produces. You should always keep this in mind when evaluating data mining algorithms.

What do you need to write an algorithm for data mining?

Before developers create a data mining algorithm, they must first know the purpose of the algorithm and what it will analyze in terms of both data type and data format. Will the algorithm examine handwriting? Will it examine cell phone photographs? Will it examine shopping tendencies?

In addition to knowing what an algorithm will examine, developers also need an appropriate set of data. Based on the application, data could vary from a collection of sample handwriting or cell phone photos to a large database, such as the history of transactions in a group of retail stores.

Finally, developers need to write an equation that enables the algorithm to test the data. This equation often includes probability and predictive analysis.

How do you measure the efficacy of an algorithm for data mining?

Different algorithms have different levels of efficacy. Testing efficacy sometimes means running data through multiple data mining algorithms in order to see which one produces the best results.

One study in the medical field compared different data mining algorithms’ ability to predict heart disease. When scientists ran data through various algorithms to test for heart disease prevalence, the algorithms produced different results. Some algorithms produced more accurate information and thus proved more useful than others.

Some researchers recommend high-utility itemset mining as a very efficient data mining technique. In this type of data mining, an algorithm searches sets of data for items of high importance to the user. Highly important items might include, for example, specific business transactions, exact medical files, or personal security information.

The development of this type of data mining points to the advancing functionality and promising future of the field. As the world becomes more technologically reliant, more and more data become available. This creates more opportunity for data analysis solutions.

To stay up to date on the latest developments in data mining solutions, check out the IEEE Xplore digital library . Xplore is one of the world’s largest collections of technical literature in engineering, computer science, and related technologies, with five million documents now available in its vast repository. You can search through this library to find out more about ongoing advances in data mining.

Best algorithms for data mining

As mentioned earlier, data mining algorithms fit within the broader category of learning algorithms. Typically, learning algorithms depend on either classification or regression to produce results.

What are the most-used data mining algorithms?

Classification and regression algorithms remain the most-used data mining algorithms available today.

Classification algorithms take data and separate it into groups. Usually the groups correspond to answers to questions, such as “yes” or “no.” Spam filters in email provide a good example of a classification algorithm at work. As an email comes in, an algorithm analyzes its contents (such as sender, subject, and message). Then, the algorithm files the email into either a “yes spam” or “no spam” category.

Examples of classification algorithms are naive Bayes and k-nearest neighbors. (However, you can also use a k-nearest neighbors algorithm as part of a regression model.)

Naive Bayes algorithms use Bayes’s theorem of probability to review data and assign certain classifications to it. For instance, a naive Bayes algorithm might analyze a text to determine its main theme. It might determine, for example, that a text is discussing cats or dogs.

K-nearest neighbors algorithms are some of the simplest and most easy-to-use data mining algorithms today. They have been around since the 1970s. Their main goal is to place a data point into a certain category based on the data around it.

Examples of systems using k-nearest neighbors algorithms include recommendation lists from streaming services such as Netflix or Hulu. These lists take data points (such as movies or TV shows) and recommend similar/related content to users.

Regression algorithms, on the other hand, answer more complex questions related to a data set. Their goal is to discern a relationship between different data points. For example, facial recognition software uses a regression algorithm that gathers and analyzes different data points to verify a person’s identity.

An example of a regression algorithm is a neural network. Neural networks mimic the human brain’s neural paths. Thousands or millions of pieces of information form these complex computer systems. Neural networks use linear regression algorithms to arrive at key decisions.

Both regression and classification models get support from support vector machines (SVMs). An SVM is another type of algorithm. It takes data from regression and classification models and creates graphs from the data. This lends a visual component to the algorithm. SVMs also help separate data into different classifications.

What makes a data mining algorithm popular?

Companies use data mining algorithms to solve many different problems. Consequently, a wide variety of data mining algorithms exist today. You can fine-tune each algorithm to solve a particular problem.

Generally speaking, a data mining algorithm’s popularity hinges on its ability to provide detailed answers to questions concerning big data. These answers can help users predict an event or trend or, more broadly, the future of an industry. But they also help users with tasks such as avoiding spam in their email inboxes or choosing a nightly TV show.

How do algorithms vary from data mining project to project?

All in all, algorithms are versatile. Likewise, their use varies across many projects in different industries. Some projects call for specific types of algorithms. For example, one project might require an algorithm that can test for classification-based outcomes. Another might require an algorithm that can test for regression-based outcomes.

Additionally, some projects depend on multiple algorithms to work. For instance, the results from one algorithm might help produce results that are used by a second algorithm.

Top software packages for using data mining algorithms

Today, companies often choose to invest in software packages that make data mining easy and approachable. Many of these software packages offer the added bonus of providing data managing and storage services in addition to data mining algorithms and tools.

As we have stated above, data mining algorithms vary according to their intended purpose. As such, users should choose a data mining software package that fits with their specific needs.

What software is available for using data mining algorithms?

Software packages reduce the need to produce algorithms from scratch. Likewise, they provide different data analytics tools that aid algorithms and help the user get desired results. Examples of such tools include artificial intelligence and predictive analytics.

Popular software packages such as Alteryx Analytics, Orange, and KNIME contain data analytics tools like these. They also contain additional features that appeal to users. These include, for example, data visualization and display features and accessibility across multiple platforms.

What should you look for in software for using data mining algorithms?

You should keep your goals in mind when considering software options. When you choose software, you should make sure its offerings match your data mining vision. For example, you might want a system that creates visual displays, such as charts and graphs, from a data mining algorithm’s output information. In this case, you want to make sure the software you invest in includes data visualization among its features.

Likewise, you should consider the package’s accessibility options. For instance, can Mac and PC users access the software equally as easily? Is there a cloud-based storage system or a Software-as-a-Service (SaaS) option? What does the package’s interface look like? How would the interface affect your ability to explore and utilize the software?

Furthermore, you might benefit from a software package that you can add paid or free features to over time. Some software packages allow users with a valid product license to freely download or purchase additional features. The future of data mining looks promising. Because of this, having the ability to add features might be especially important going forward.

What are the best free and paid options for data mining algorithm software?

Software packages and their offerings vary according to their monetary value. Often paid-for packages include more high-tech, innovative, and appealing elements. In contrast, free versions generally contain fewer features. However, quality free options do exist.

According to a conference paper on free software tools for data mining, the best free offerings include RapidMiner, Weka, R, KNIME, Orange, and scikit-learn. Many of the companies behind these free tools also offer data mining services.

Paid-for options include Sisense, Neural Designer, and Alteryx Analytics. These companies focus on different data mining tools, such as analytics, machine learning, and business intelligence, respectively.

Ultimately, as technology continues to improve, the variety of data mining algorithms and software packages will likely continue to grow. So too will the importance and potential value of data mining as a field continue to grow in the future.

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Data Mining and Big Data Analytics Technical Committee

The Data Mining and Big Data Analytics Technical Committee (DMTC) is established to: (1) promote the research, development, education and understanding the principles and applications of data mining and big data analytics and (2) to help researchers whose background is primarily in computational intelligence in increasing their contributions to this area.

The DMTC shall engage in various activities in order to advance the goals described above, including but not limited to the following:

• Identify and promote new areas of research
• Propose special sessions to the CIS-sponsored conference organizers
• Publicize success stories on solving real data mining problems
• Participate in paper review and selection for CIS-sponsored conferences and publications
• Recommend candidates/papers for awards and collaborate on production of tutorials and book series with the Multimedia Committee
• The DMTC will also assist in soliciting proposals for focused workshops or special sessions and actively work with the organizers of CIS-sponsored conferences to ensure their technical excellence

Committee Membership

• Grant Scott, Chair
• Ata Kaban and Handing Wang, Vice Chairs

Current directory of Chairs and members: Data Mining and Big Data Analytics Technical Committee Members

DMTC members are appointed to serve one year term. Reappointment is allowed.

Chair is appointed by CIS President.  2 Vice Chairs are appointed by Chair and endorsed by CIS VP of Technical Activities.  TC Members are appointed by Chair and endorsed by CIS VP of Technical Activities.  (Number of members varies, typically less than 20)

If you are interested in being considered for a position on the  Data Mining and Big Data Analytics Technical Committee , or would like more information on the activities of the committee, send an email to [email protected] .   If you are interested in being considered for a position on the committee, please include a link to your CV.

Task Forces

• Chair: Longzhi Yang
• Chair: Longbin Cao
• Chair: Gang Li
• Chair:  Sandra Ortega-Martorell
• Chair:  Boudewijn van Dongen
• Chair: Weiping Ding
• Chair: Kohei Nakajima
• Chair:  Alfredo Vellido
• Chair: Ata Kaban

Updated: 5/9/2024

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• Addresses theory, foundational issues, data mining methods, and knowledge discovery processes.
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Data Mining for Internet of Things

Submission Deadline: 31 March 2020

IEEE Access invites manuscript submissions in the area of Data Mining for Internet of Things.

The Internet of Things (IoT) has become an important research domain as mature appliances, systems, infrastructures, and their applications have shown their potential in recent years. We can foresee that smart homes and smart cities using these technologies will be realized in the near future. However, many consumers have concerns with the “smart” information system and environment, especially when entering the era of IoT. The expectations of IoT and its relevant products in this new era are quite high. Instead of smartness alone, consumers of IoT products and services would like to see IoT technologies bring about more intelligent systems and environments. The main difference between the “smart thing” and “intelligent thing” is that the former will use predefined rules to provide services to a user whereas the latter will not only use predefined rules, but will also use the analytical results from intelligent mechanisms to discover suitable services for users. More precisely, using only the predefined rules may not be sufficient to consider every possible situation because the number of rules is limited. Using the results obtained after data analysis we can provide additional information to an IoT system to make it better understand the needs of a user. This is why data analytics has become a promising technology for IoT.

Although most researchers of data mining have recognized how to analyze large-scale data is an important research topic for many years, considerations for the IoT environment are quite different from those for the traditional environment because data for the IoT will be created more quickly and in different formats. That is why research on data analytics for IoT have typically been relevant to big data analytics and cloud computing technologies in recent years. This does not mean that traditional data mining and intelligent algorithms are no longer useful for IoT. In fact, how to redesign these algorithms to make them more efficiently and effectively work for IoT has been a critical research trend. In addition to modifying the traditional data mining and intelligent algorithms, an alternative is to develop new data analysis algorithms. Using deep learning technologies for supervised learning to construct a set of classifiers to recognize data entering an IoT system, and using metaheuristic algorithms for unsupervised learning to find out good solutions for classifying unknown data are two promising technologies today. Moreover, how to determine interesting patterns from a series of events of an IoT system is also a critical research topic. In summary, many modern technologies, such as big data analytics, statistical technologies, and other analysis technologies, have also been used for finding out useful information from an IoT system to provide needed services to a user and to enhance the performance of the IoT system as a whole today.

This Special Section in IEEE Access will focus on data mining technologies for the IoT and its applications, such as smart home, smart city, industry, online social network, and even internet of vehicles. We also welcome research on IoT related technologies, such as cloud computing, network security, wireless sensor network, vehicular networking, smart grids, and big data.

The topics of interest include, but are not limited to:

• Data Mining for the IoT
• Machine and Deep Learning for the IoT
• Metaheuristic Algorithms for the IoT
• Cloud Computing for the IoT
• Big Data for the IoT
• Mobile Computing and Sensing for the IoT
• Security Framework for the IoT
• Privacy Protection for the IoT
• IoT in Smart Home and Smart City
• IoT in Energy Management
• Industry IoT
• IoT in Agriculture and Environment
• IoT in eHealth and Ambient Assisted Living
• Internet of Vehicles
• Edge Computing
• Applications of the IoT

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

Associate Editor:  Chun-Wei Tsai, National Chung-Hsing University, Taiwan

Guest Editors:

• Mu-Yen Chen, National Taichung University of Science and Technology, Taiwan
• Francesco Piccialli, University of Naples Federico II, Italy
• Tie Qiu, Tianjin University, China
• Jason J. Jung, Chung-Ang University, Republic of Korea
• Patrick C. K. Hung, University of Ontario Institute of Technology, Canada
• Sherali Zeadally, University of Kentucky, USA

Relevant IEEE Access Special Sections:

• Smart Caching, Communications, Computing and Cybersecurity for Information-Centric Internet of Things
• Healthcare Information Technology for the Extreme and Remote Environments
• Internet-of-Things (IoT) Big Data Trust Management

IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

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For inquiries regarding this Special Section, please contact: [email protected] .

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Distance Based Pattern Driven Mining for Outlier Detection in High Dimensional Big Dataset

Detection of outliers or anomalies is one of the vital issues in pattern-driven data mining. Outlier detection detects the inconsistent behavior of individual objects. It is an important sector in the data mining field with several different applications such as detecting credit card fraud, hacking discovery and discovering criminal activities. It is necessary to develop tools used to uncover the critical information established in the extensive data. This paper investigated a novel method for detecting cluster outliers in a multidimensional dataset, capable of identifying the clusters and outliers for datasets containing noise. The proposed method can detect the groups and outliers left by the clustering process, like instant irregular sets of clusters (C) and outliers (O), to boost the results. The results obtained after applying the algorithm to the dataset improved in terms of several parameters. For the comparative analysis, the accurate average value and the recall value parameters are computed. The accurate average value is 74.05% of the existing COID algorithm, and our proposed algorithm has 77.21%. The average recall value is 81.19% and 89.51% of the existing and proposed algorithm, which shows that the proposed work efficiency is better than the existing COID algorithm.

Implementation of Data Mining Technology in Bonded Warehouse Inbound and Outbound Goods Trade

For the taxed goods, the actual freight is generally determined by multiplying the allocated freight for each KG and actual outgoing weight based on the outgoing order number on the outgoing bill. Considering the conventional logistics is insufficient to cope with the rapid response of e-commerce orders to logistics requirements, this work discussed the implementation of data mining technology in bonded warehouse inbound and outbound goods trade. Specifically, a bonded warehouse decision-making system with data warehouse, conceptual model, online analytical processing system, human-computer interaction module and WEB data sharing platform was developed. The statistical query module can be used to perform statistics and queries on warehousing operations. After the optimization of the whole warehousing business process, it only takes 19.1 hours to get the actual freight, which is nearly one third less than the time before optimization. This study could create a better environment for the development of China's processing trade.

Multi-objective economic load dispatch method based on data mining technology for large coal-fired power plants

User activity classification and domain-wise ranking through social interactions.

Twitter has gained a significant prevalence among the users across the numerous domains, in the majority of the countries, and among different age groups. It servers a real-time micro-blogging service for communication and opinion sharing. Twitter is sharing its data for research and study purposes by exposing open APIs that make it the most suitable source of data for social media analytics. Applying data mining and machine learning techniques on tweets is gaining more and more interest. The most prominent enigma in social media analytics is to automatically identify and rank influencers. This research is aimed to detect the user's topics of interest in social media and rank them based on specific topics, domains, etc. Few hybrid parameters are also distinguished in this research based on the post's content, post’s metadata, user’s profile, and user's network feature to capture different aspects of being influential and used in the ranking algorithm. Results concluded that the proposed approach is well effective in both the classification and ranking of individuals in a cluster.

A data mining analysis of COVID-19 cases in states of United States of America

Epidemic diseases can be extremely dangerous with its hazarding influences. They may have negative effects on economies, businesses, environment, humans, and workforce. In this paper, some of the factors that are interrelated with COVID-19 pandemic have been examined using data mining methodologies and approaches. As a result of the analysis some rules and insights have been discovered and performances of the data mining algorithms have been evaluated. According to the analysis results, JRip algorithmic technique had the most correct classification rate and the lowest root mean squared error (RMSE). Considering classification rate and RMSE measure, JRip can be considered as an effective method in understanding factors that are related with corona virus caused deaths.

Exploring distributed energy generation for sustainable development: A data mining approach

A comprehensive guideline for bengali sentiment annotation.

Sentiment Analysis (SA) is a Natural Language Processing (NLP) and an Information Extraction (IE) task that primarily aims to obtain the writer’s feelings expressed in positive or negative by analyzing a large number of documents. SA is also widely studied in the fields of data mining, web mining, text mining, and information retrieval. The fundamental task in sentiment analysis is to classify the polarity of a given content as Positive, Negative, or Neutral . Although extensive research has been conducted in this area of computational linguistics, most of the research work has been carried out in the context of English language. However, Bengali sentiment expression has varying degree of sentiment labels, which can be plausibly distinct from English language. Therefore, sentiment assessment of Bengali language is undeniably important to be developed and executed properly. In sentiment analysis, the prediction potential of an automatic modeling is completely dependent on the quality of dataset annotation. Bengali sentiment annotation is a challenging task due to diversified structures (syntax) of the language and its different degrees of innate sentiments (i.e., weakly and strongly positive/negative sentiments). Thus, in this article, we propose a novel and precise guideline for the researchers, linguistic experts, and referees to annotate Bengali sentences immaculately with a view to building effective datasets for automatic sentiment prediction efficiently.

Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques

Studying information diffusion in SNS (Social Networks Service) has remarkable significance in both academia and industry. Theoretically, it boosts the development of other subjects such as statistics, sociology, and data mining. Practically, diffusion modeling provides fundamental support for many downstream applications (e.g., public opinion monitoring, rumor source identification, and viral marketing). Tremendous efforts have been devoted to this area to understand and quantify information diffusion dynamics. This survey investigates and summarizes the emerging distinguished works in diffusion modeling. We first put forward a unified information diffusion concept in terms of three components: information, user decision, and social vectors, followed by a detailed introduction of the methodologies for diffusion modeling. And then, a new taxonomy adopting hybrid philosophy (i.e., granularity and techniques) is proposed, and we made a series of comparative studies on elementary diffusion models under our taxonomy from the aspects of assumptions, methods, and pros and cons. We further summarized representative diffusion modeling in special scenarios and significant downstream tasks based on these elementary models. Finally, open issues in this field following the methodology of diffusion modeling are discussed.

The Influence of E-book Teaching on the Motivation and Effectiveness of Learning Law by Using Data Mining Analysis

This paper studies the motivation of learning law, compares the teaching effectiveness of two different teaching methods, e-book teaching and traditional teaching, and analyses the influence of e-book teaching on the effectiveness of law by using big data analysis. From the perspective of law student psychology, e-book teaching can attract students' attention, stimulate students' interest in learning, deepen knowledge impression while learning, expand knowledge, and ultimately improve the performance of practical assessment. With a small sample size, there may be some deficiencies in the research results' representativeness. To stimulate the learning motivation of law as well as some other theoretical disciplines in colleges and universities has particular referential significance and provides ideas for the reform of teaching mode at colleges and universities. This paper uses a decision tree algorithm in data mining for the analysis and finds out the influencing factors of law students' learning motivation and effectiveness in the learning process from students' perspective.

Intelligent Data Mining based Method for Efficient English Teaching and Cultural Analysis

The emergence of online education helps improving the traditional English teaching quality greatly. However, it only moves the teaching process from offline to online, which does not really change the essence of traditional English teaching. In this work, we mainly study an intelligent English teaching method to further improve the quality of English teaching. Specifically, the random forest is firstly used to analyze and excavate the grammatical and syntactic features of the English text. Then, the decision tree based method is proposed to make a prediction about the English text in terms of its grammar or syntax issues. The evaluation results indicate that the proposed method can effectively improve the accuracy of English grammar or syntax recognition.

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DATA MINING IEEE PAPERS AND PROJECTS-2020

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems

Data stream mining , as its name suggests, is connected with two basic fields of computer science, ie data mining and data streams. Data mining [1 4] is an interdisciplinary subfield of computer science whose main aim is to develop tools and methods for exploring

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