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DATA MINING TECHNIQUES AND APPLICATIONS Mrs. Bharati M. Ramageri, Lecturer Modern Institute of Information Technology and Research, Department of Computer Application, Yamunanagar, Nigdi Pune, Maharashtra, India Abstract Data mining is a process which finds useful patterns from large amount of data. The paper discusses few of the data miningFile Size: KB. Web mining is one of the mining technologies, which applies data mining techniques in large amount of web data to improve the web services. Web traversal pattern mining discovers most of the users Estimated Reading Time: 7 mins. 30/04/ · They are used in a lot of applications. Here are a few of those data types: data streams, engineering design data, sequence data, graph data, spatial data, multimedia data, and more. Data Mining Techniques 1. Association. It is one of the most used data mining techniques out of Estimated Reading Time: 7 mins. Data Mining Algorithms and Techniques Various algorithms and techniques like Classification, Clustering, Regression, Artificial Intelligence, Neural Networks, Association Rules, Decision Trees, Genetic Algorithm, Nearest Neighbor method etc., are used for knowledge discovery from databases.

Skip to Main Content. A not-for-profit organization, IEEE is the world’s largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Data mining techniques and applications — A decade review Abstract: Data mining is also known as Knowledge Discovery in Database KDD.

It is also defined as the process which includes extracting the interesting, interpretable and useful information from the raw data. There are different sources that generate raw data in very large amount. This is the main reason the applications of data mining are increasing rapidly. This paper reviews data mining techniques and its applications such as educational data mining EDM , finance, commerce, life sciences and medical etc.

We group existing approaches to determine how the data mining can be used in different fields. Our categorization specifically focuses on the research that has been published over the period

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Data mining is a process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. In simple words, data mining is defined as a process used to extract usable data from a larger set of any raw data. It implies analyzing data patterns in large batches of data using one or more software techniques.

In recent years data mining techniques are used to process the growing amount of data piling up from many resources. These techniques process efficiently in many areas of human activities such as the Genome Project. The Human Genome Project was an international scientific research project with the goal of determining the base pairs that make up human DNA, and of identifying and mapping all of the genes of the human genome from both a physical and a functional standpoint.

The Project has provided researchers all over the world with a large set of data containing valuable information that needs to be discovered. The code that codifies life has been read, but it is not yet known how life works. It is needed to know the relationship between the genes and how they interact and affect one another. Another important data set is produced by web crawlers on the internet.

Discovering interesting patterns in the documents available from web pages also may help for the welfare of human activities. Discovering interesting patterns in the chaotic interconnection of web pages helps in finding useful relationships for web searching purposes.

data mining techniques and applications

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DOI: Periodicity: Bi Annual. Impact Factor: SJIF Author can submit their paper through online submission. Click here. Every issue of Journal of IJDMTA is available online from volume 1 issue 1 to the latest published issue with month and year. The Group of paper will accept with some concession and will publish in IJDMTA website. For complete procedure, contact us at admin iirgroups. International Journal of Data Mining Techniques and Applications IJDMTA is a peer-reviewed bi-annual journal that publishes high-quality papers on all aspects of IJDMTA.

The primary objective of IJDMTA is to be an authoritative International forum for delivering both theoretical and innovative applied researches in the data mining concepts, to implementations. The IJDMTA publishes original research contributions, surveys, and experimental studies with technical advances. The IJDMTA is publishes research and technical papers, both short and long. We welcome authors to submit the research and technical papers in the following area.

International Journal of Data Mining Techniques and Applications IJDMTA Print ISSN

data mining techniques and applications

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To browse Academia. Log In with Facebook Log In with Google Sign Up with Apple. Remember me on this computer. Enter the email address you signed up with and we’ll email you a reset link. Need an account? Click here to sign up. Download Free PDF. Predicting Customers‘ Churn Using Data Mining Technique and its Effect on the Development of Marketing Applications in Value-Added Services in Telecom Industry International Journal of Information Systems in the Service Sector.

Parna Saeidpour. Download PDF Download Full PDF Package This paper. A short summary of this paper. Predicting Customers‘ Churn Using Data Mining Technique and its Effect on the Development of Marketing Applications in Value-Added Services in Telecom Industry. In this study, different data mining techniques such as logistic regression, decision trees, artificial neural networks, and K-nearest neighbor were examined.

KEywoRDS Churn of Customers, Customer Classification, Data Mining, KNN Algorithm, Mobile Telecom Market INTRoDUCTIoN In recent decades, the human ability to generate and collect data quickly has increased.

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To browse Academia. Log In with Facebook Log In with Google Sign Up with Apple. Remember me on this computer. Enter the email address you signed up with and we’ll email you a reset link. Need an account? Click here to sign up. Download Free PDF. Prakash Durai. Download PDF Download Full PDF Package This paper. A short summary of this paper. Bharati M.

data mining techniques and applications

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Abhinav is a Data Analyst at UpGrad. He’s an experienced Data Analyst with a demonstrated history of working in the higher education industry. Strong information technology professional skilled in Python,…. Today a majority of organizations and institutions gather and store massive amounts of data in data warehouses, and cloud platforms and this data continues to grow exponentially by the minute as new data comes pouring in from multiple sources.

As a result, it becomes a challenge for companies and organizations to store, process, and analyze vast amounts of textual data with traditional tools. This is where text mining applications, text mining tools , and text mining techniques come in. No Coding Experience Required. Text mining incorporates and integrates the tools of information retrieval, data mining, machine learning, statistics, and computational linguistics, and hence, it is nothing short of a multidisciplinary field.

Text mining deals with natural language texts either stored in semi-structured or unstructured formats. Text mining techniques can be understood at the processes that go into mining the text and discovering insights from it. These text mining techniques generally employ different text mining tools and applications for their execution.

Now, let us now look at the various text mining techniques:. Information Extraction.

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The fact that big data and analytics change the business and science world is undeniable. But how? What are data mining applications, examples, and benefits? Data scientists already saw how machine learning and the uses of data mining techniques deliver results. Simply, data mining is the process of finding patterns, trends, and anomalies within large data sets to take adequate decisions and to predict outcomes.

Now, there is an enormous amount of data available anywhere, anytime. But this data is worthless for the management decisions until it is turned into useful information. This is where data mining comes to play. It turns raw unstructured data into useful information. Through a wide range of techniques and statistical algorithms, data mining is able to help businesses increase revenues, reduce costs, or answer questions that bother many other industries.

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08/09/ · This paper reviews data mining techniques and its applications such as educational data mining (EDM), finance, commerce, life sciences and medical etc. We group existing approaches to determine how the data mining can be used in different fields. Data mining is a process which finds useful patterns from large amount of data. The paper discusses few of the data mining techniques, algorithms and some of the organizations which have adapted data mining technology to improve their businesses and found excellent results.

Skip to Main Content. A not-for-profit organization, IEEE is the world’s largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. A Survey on Trajectory Data Mining: Techniques and Applications Abstract: Rapid advance of location acquisition technologies boosts the generation of trajectory data, which track the traces of moving objects.

A trajectory is typically represented by a sequence of timestamped geographical locations. A wide spectrum of applications can benefit from the trajectory data mining. Bringing unprecedented opportunities, large-scale trajectory data also pose great challenges. In this paper, we survey various applications of trajectory data mining, e.

Furthermore, this paper reviews an extensive collection of existing trajectory data mining techniques and discusses them in a framework of trajectory data mining. This framework and the survey can be used as a guideline for designing future trajectory data mining solutions.

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