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Transactions on Computational Science XXI pp 164–189Cite as

Categorical Feature Reduction Using Multi Objective Genetic Algorithm in Cluster Analysis

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Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 8160))

Abstract

In the paper, real coded multi objective genetic algorithm based K-clustering method has been studied, K represents the number of clusters. In K-clustering algorithm value of K is known. The searching power of Genetic Algorithm (GA) is exploited to search for suitable clusters and centers of clusters so that intra-cluster distance (Homogeneity, H) and inter-cluster distances (Separation, S) are simultaneously optimized. It is achieved by measuring H and S using Mod distance per feature metric, suitable for categorical features (attributes). We have selected 3 benchmark data sets from UCI Machine Learning Repository containing categorical features only.

The paper proposes two versions of MOGA based K-clustering algorithm. In proposed MOGA (H, S), all features are taking part in building chromosomes and calculation of H and S values. In MOGA_Feature_Selection (H, S), selected features take part to build chromosomes, relevant for clusters. Here, K-modes is hybridized with GA. We have used hybridized GA to combine global searching capabilities of GA with local searching capabilities of K-modes. Considering context sensitivity, we have used a special crossover operator called “pairwise crossover” and “substitution”. The main contribution of this paper is simultaneous dimensionality reduction and optimization of objectives using MOGA.

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Authors and Affiliations

  1. Department of Computer Science and Information Technology, University Institute of Technology, The University of Burdwan, Golapbug (North), Burdwan, West Bengal, 713104, India

    Dipankar Dutta

  2. Department of Computer and System Sciences, Visva-Bharati University, Santiniketan, West Bengal, 731235, India

    Paramartha Dutta

  3. Department of Computer Science and Technology, Bengal Engineering and Science University, Shibpur, Howrah, West Bengal, 711103, India

    Jaya Sil

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  1. University of Calgary, AB, Canada

    Marina L. Gavrilova

  2. CloudFabriQ Ltd., London, UK

    C. J. Kenneth Tan

  3. Technical University of Ostrava, Czech Republic

    Ajith Abraham

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Dutta, D., Dutta, P., Sil, J. (2013). Categorical Feature Reduction Using Multi Objective Genetic Algorithm in Cluster Analysis. In: Gavrilova, M.L., Tan, C.J.K., Abraham, A. (eds) Transactions on Computational Science XXI. Lecture Notes in Computer Science, vol 8160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45318-2_7

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