Abstract Feature selection is the process of selecting a subset of optimal features by removing redundant and irrelevant features. In supervised learning, feature selection process uses class label. But feature selection is difficult in unsupervised learning since class labels are not present. In this paper, we present a wrapper based unsupervised feature selection method with the modified binary bat approach with k-means clustering algorithm. To ensure diversification in the search space, mutation operator is introduced in the proposed algorithm. To validate the selected features by our method, classification algorithms like decision tree induction, Support Vector Machine and Naïve Bayesian classifier are used. The results show that the proposed method identifies a minimal number of features with improved accuracy when compared with the other methods.

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[CSE] and ME

[CSE] from Bharathiar University during 1990 and 2001 respectively. She completed her Ph.D in Anna university, India during 2011. She is working as Professor in CSE, Kongu Engineering College, India. Her research interests include Data Mining, Nature Inspired Computing and Big Data Analytics. Sylvia Rani completed B.E

[CSE] during 2009. She obtained M.E

[CSE] from Kongu Engineering College, Tamilnadu, India in 2015.She has published two papers in national and international conference. Her research interests include Nature inspired computing and Parallel Processing.