Paper Details: Downloads: 529
Serial Number: P1120548004
Title: Application of Clustering for Feature Selection Based on Rough Set Theory Approach
Authors: K.Thangavel and Qiang Shen and A. Pethalakshmi
Abstract: Unsupervised clustering is an essential technique in Datamining. Since feature selection is a valuable technique in data analysis for information preserving data reduction, researchers have made use of the rough set theory to construct reducts by which the unsupervised clustering is changed into the supervised reduct. Rule identification involves the application of Datamining techniques to derive usage patterns from the information system. Knowledge extraction from data is the key to success in many fields. Knowledge extraction techniques and tools can assist humans in analyzing mountains of data and to turn the information contained in the data into successful decision making. This paper proposes, to consider an information system without any decision attribute. The proposal is useful when we get data, which contains only input information (condition attributes) but without decision (class attribute). K-Means algorithm is applied to cluster the given information system for different values of K. Decision table could be formulated using this clustered data as the decision variable. Then Quickreduct and VPRS algorithms are applied for selecting features. Ultimately, Rule Algorithm is used for obtaining optimum rules. The experiments are carried out on data sets of UCI machine learning repository and the HIV data set to analyze the performance study.
Keywords: Datamining, K-Means Clustering, Rough set, Reduct and Rule induction.
Journal/Conference: International Journal of Artificial Intelligence and Machine Learning
Volume: 6
Issue: 1
Submission Date: 11/1/2005 12:00:00 AM
Review Date: 12/1/2005 12:00:00 AM
Publishing Date: 1/1/2006 12:00:00 AM
Article Downloads: 529
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