2019•Journal of Emerging Technologies and Innovative ResearchRequires access

A Complete Study of Algorithm Selection in Data Mining

N. Krishnaveni, R.Waheetha

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Abstract

Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data set. These tools can include statistical models, mathematical algorithm and machine learning methods. Consequently, data mining consists of more than collection and managing data, it also includes analysis and prediction. This paper puts forward the most used data mining algorithms used in the research field. With each algorithm, a basic explanation is given with a real time example, and each algorithms pros and cons are weighed individually. These algorithms are seen in some of the most important topics in data mining research and development such as classification, clustering, statistical learning, association analysis, and link mining.

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What this paper is about

Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data set. These tools can include statistical models, mathematical algorithm and machine learning methods. Consequently, data mining consists of more than collection and managing data, it also includes analysis and prediction. This paper puts forward the most used data mining algorithms used in the research field. With each algorithm, a basic explanation is given with a real time example, and each algorithms pros and cons are weighed individually. These algorithms are seen in some of the most important topics in data mining research and development such as classification, clustering, statistical learning, association analysis, and link mining.

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Available abstract

Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data set. These tools can include statistical models, mathematical algorithm and machine learning methods. Consequently, data mining consists of more than collection and managing data, it also includes analysis and prediction. This paper puts forward the most used data mining algorithms used in the research field. With each algorithm, a basic explanation is given with a real time example, and each algorithms pros and cons are weighed individually. These algorithms are seen in some of the most important topics in data mining research and development such as classification, clustering, statistical learning, association analysis, and link mining.

Key concepts: Data mining, Computer science, Cluster analysis, Field (mathematics), Selection (genetic algorithm), Data stream mining, Association rule learning, Set (abstract data type)

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