International Journal of Emerging Trends & Technology in Computer Science
A Motivation for Recent Innovation & Research
ISSN 2278-6856
www.ijettcs.org

Call for Paper, Published Articles, Indexing Infromation Comparative Analysis of Classification Techniques in Data Mining, Authors : V.Saranya , A.Vigneswari, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), www.ijettcs.org
Volume & Issue no: Volume 9, Issue 1, January - February 2020

Title:
Comparative Analysis of Classification Techniques in Data Mining
Author Name:
V.Saranya , A.Vigneswari
Abstract:
Abstract: Nowadays, an enormous library of Data Mining techniques has been extended to carry out a stacks of trouble in fields such as medical imaging, sales, business administration, marketing and traffic analysis, manufacturing process astronomy and etc. Currently, Data Mining had a major force on the information industry, due to the broad availability of immense datasets. Classification technique is one kind of generally applied process of data mining in healthcare. Classification is frequently used in marketing, surveillance, fraud detection and scientific discovery. This paper compared the a few classification algorithm gives the best result. The researchers applied a variety of classification algorithms such as K-Nearest Neighbour classifiers, decision Tree, Bayesian Network, Support Vector Machine and Artificial Neural Networks. This paper presents the comparative analysis on different classification algorithms such as Naive Bayes, IBK, Decision Tree and J48. The experimental result shows that the Naïve Bayes classification algorithm gives high classification accuracy than the rest of the algorithms. These algorithms are evaluated by precision, f-measures, recall, TP Rate and FP Rate. Keywords: Data mining, Classification, Decision Tree, and Bayesian Network.
Cite this article:
V.Saranya , A.Vigneswari , " Comparative Analysis of Classification Techniques in Data Mining " , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 9, Issue 1, January - February 2020 , pp. 001-005 , ISSN 2278-6856.
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