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2013 (Vol. 6, Issue: 24)
Article Information:

Improving Efficiency of Classification using PCA and Apriori based Attribute Selection Technique

K. Rajeswari, Rohit Garud and V. Vaithiyanathan
Corresponding Author:  K. Rajeswari 

Key words:  Apriori, bayes, classification, data mining, decision tree classifier j48, features, mult layer perceptron, WEKA 3.6.0
Vol. 6 , (24): 4681-4684
Submitted Accepted Published
April 09, 2013 May 03, 2013 December 25, 2013
Abstract:

The aim of this study is to select significant features that contribute for accuracy in classification. Data mining is a field where we find lots of data which can be useful or useless in any form available in Data Warehouse. Implementing classification on these huge, uneven, useless data sets with large number of features is just a waste of time degrading the efficiency of classification algorithms and hence the results are not much accurate. Hence we propose a system in which we first use PCA (Principal Component Analysis) for selection of the attributes on which we perform Classification using Bayes theorem, Multi-Layer Perceptron, Decision tree J48 which indeed has given us better result than that of performing Classification on the huge complete data sets with all the attributes. Also association rule mining using traditional Apriori algorithm is experimented to find out sub set of features related to class label. The experiments are conducted using WEKA 3.6.0 Tool.
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  Cite this Reference:
K. Rajeswari, Rohit Garud and V. Vaithiyanathan , 2013. Improving Efficiency of Classification using PCA and Apriori based Attribute Selection Technique.  Research Journal of Applied Sciences, Engineering and Technology, 6(24): 4681-4684.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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