Classification Algorithms in Data Mining : A Survey

Authors(2) :-C. Parimala, R. Porkodi

Classification is a data mining task that assigns items in a collection to target categories or classes. The scope of classification is to accurately predict the target class for each case in the data. In the hypothesis build training procedure, a classification algorithm find relationships between the worth of the predictors and the values of the goal. Different classification algorithms use dissimilar techniques for finding relationships. These relationships are summarized in a model, which container afterward be apply to a different data set in which the class assignments are unknown. Classification has many applications in customer segmentation, business modeling, marketing, credit analysis, bio medical and drug responsemodeling. This paper presents the study and analysis of five classification algorithms manually Bayesian network, j48, logistic model tree, random tree and rep tree for liver disorders dataset and the performance of these algorithms are compared using the various performance metrics such as Precision, Recalland F measure in which random tree algorithm gives 100% accuracy. The experimental result shows that random tree provides high accuracy than the Bayesian algorithm, j48, logistic model tree and rep tree.

Authors and Affiliations

C. Parimala
PG Scholar, Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India
R. Porkodi
Assistant Professor, Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India

Classification Algorithm, Bayesian Net, J48, LMT, Random Tree, REP Tree.

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Publication Details

Published in : Volume 3 | Issue 1 | January-February 2018
Date of Publication : 2018-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 349-355
Manuscript Number : CSEIT183128
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

C. Parimala, R. Porkodi, "Classification Algorithms in Data Mining : A Survey", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.349-355, January-February-2018.
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