Affiliations: [a] Department of Computer Science and Information Technology, University Institute of Technology, The University of Burdwan, Golapbug (North), Burdwan, West Bengal, India | [b] Department of Computer and System Sciences, Visva-Bharati University, Santiniketan, West Bengal, India | [c] Department of Computer Science and Technology, Bengal Engineering and Science University, Shibpur, Howrah, West Bengal, India
Correspondence:
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Corresponding author: Dipankar Dutta, Department of Computer Science and Information Technology, University Institute of Technology, The University of Burdwan, Golapbug (North), Burdwan, West Bengal, PIN-713104, India. Tel.: +91 9832115594; E-mail: [email protected]
Abstract: In this paper, we propose a novel evolutionary clustering algorithm for mixed type data (numerical and categorical). It is doing clustering and feature selection simultaneously. Feature subset selection improves quality of clustering. It also improves understandability and scalability. It unfastens attraction on numerical or categorical dataset only. K-prototype (KP) is a well-known partitional clustering algorithm for mixed type data. However, this type of algorithm is sensitive to initialization and may converge to local optima. It is optimizing a single measure only i.e. minimizations of intra cluster distance. We have considered clustering as a multi objective optimization problem (MOOP). Minimization of intra cluster distance and maximization of inter cluster distance are two objectives of optimization. Multi objective genetic algorithm (MOGA) is a well-known algorithm which can be applicable for MOOP to find out near global optimal solution. So in this paper we have developed a hybridized genetic clustering algorithm by combining the global search ability of MOGA and local search ability of KP. Experiments on real-life benchmark datasets from UCI machine learning repository prove the superiority of the proposed algorithm.
Keywords: Clustering, multi objective genetic algorithm (MOGA), K-prototype (KP), mixed type data, feature selection, hybridization
