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.

(Full Paper, 876KB)

Biography

THANGAVEL KUTTIANNAN, received the master degree of science from the Department of Mathematics, Bharthidasan University in 1986 and Master of Computer Applications degree from Madurai Kamaraj University, Madurai in 2001. He obtained his Ph.D degree from Gandhigram Rural Institute-Deemed University, Gandhigram, Tamilnadu, India in 1996. Currently he is working as a Professor of Computer Science, Periyar University, Salem, Tamilnadu, India. He is in teaching since 1989.  His areas of interest include Data mining, Medical Image Processing, Genetic Algorithms, Neural Network, Rough Set Theory, Machine Learning and Artificial Intelligence. ktvel@rediffmail.com

PETHALAKASHMI ANNAMALAI, received the Master of Computer Science degree from Alagappa University, Tamil Nadu, India, in 1988 and the Master of Philosophy in Computer Science from Mother Teresa Women’s University, Tamil Nadu, India, in 2000. She is   working as Selection Grade Lecturer, Department of Computer Science, M.V.M. Govt. Arts College (w), Dindigul, Tamil Nadu, India, and currently she is the full-time research scholar in the Department of Computer Science, Mother Teresa Women’s University, Tamil Nadu, India. She is in teaching since 1989.  Her areas of interest include  Data mining, Fuzzy Logic, Rough Set Theory and Neural Network.

BibTex:

@ARTICLE{P1120548004,

AUTHOR = {K.Thangavel and  Qiang Shen and  A. Pethalakshmi^3*},

TITLE = {Application of Clustering for Feature Selection Based on Rough Set Theory Approach},

}

(Full Paper, 876KB)