Data Mining and Knowledge Engineering

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Comparative Analysis on Classification Data Mining Techniques Through WEKA


Affiliations
1 Department of Information Technology, Datta Meghe Institute of Engineering, Technology & Research, Wardha, India
2 Department of Information Technology, SGMCOE, Shegao, India
3 Cluebix Software Pvt. Ltd, Nagpur, India
     

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Data mining refers to extraction or mining of information/knowledge from huge amounts of data. Data mining is also called as Knowledge Discovery from Database (KDD). There are number of data mining techniques such as classification and regression; the mining of frequent patterns, associations, and correlations; clustering analysis; and outlier analysis.
Classification is a major technique in data mining which is widely used in various fields.
Classification can be defined as the process of finding a model (or function) that describes and distinguishes data classes or concepts, for the purpose of being able to use the function/model to guess the class of objects whose class label is unknown. The derived model is based on the analysis of a set of training data (i.e., data objects whose class label is known). Classification models predict categorical class labels. Classification may also called as supervised Learning.
In this paper, we are going to discuss the various techniques of classification. Different kinds of classification techniques includes if-then rule, decision tree, Neural Network. Bayesian networks, k-nearest neighbor classifier, and support vector machine (SVM), and the aim of this study is to provide a comprehensive review of different classification techniques in data mining.

Keywords

Classification, Data Mining, Decision Tree, Supervised Learning, WEKA, Pattern Evaluation, Neural Network, KDD.
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  • Comparative Analysis on Classification Data Mining Techniques Through WEKA

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Authors

Lukeshkumar Barapatre
Department of Information Technology, Datta Meghe Institute of Engineering, Technology & Research, Wardha, India
Anand Sharma
Department of Information Technology, SGMCOE, Shegao, India
Hemant Barapatre
Cluebix Software Pvt. Ltd, Nagpur, India

Abstract


Data mining refers to extraction or mining of information/knowledge from huge amounts of data. Data mining is also called as Knowledge Discovery from Database (KDD). There are number of data mining techniques such as classification and regression; the mining of frequent patterns, associations, and correlations; clustering analysis; and outlier analysis.
Classification is a major technique in data mining which is widely used in various fields.
Classification can be defined as the process of finding a model (or function) that describes and distinguishes data classes or concepts, for the purpose of being able to use the function/model to guess the class of objects whose class label is unknown. The derived model is based on the analysis of a set of training data (i.e., data objects whose class label is known). Classification models predict categorical class labels. Classification may also called as supervised Learning.
In this paper, we are going to discuss the various techniques of classification. Different kinds of classification techniques includes if-then rule, decision tree, Neural Network. Bayesian networks, k-nearest neighbor classifier, and support vector machine (SVM), and the aim of this study is to provide a comprehensive review of different classification techniques in data mining.

Keywords


Classification, Data Mining, Decision Tree, Supervised Learning, WEKA, Pattern Evaluation, Neural Network, KDD.

References