A new multiobjective simulated annealing based clustering technique using symmetry

Authors:
Sriparna Saha;Sanghamitra Bandyopadhyay
Affiliations:
Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700108, India;Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700108, India
Venue:
Pattern Recognition Letters
Year:
2009

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Abstract

In this paper, we have proposed a multiobjective clustering technique which optimizes simultaneously two objectives, one reflecting the total 'goodness' present in the data set in terms of total compactness (measured using Euclidean distance) of the clusters, and the other reflecting the total symmetry present in the clusters of the data set. The proposed algorithm uses a simulated annealing based multiobjective optimization method as the underlying optimization criterion. Center based encoding is used. The proposed multiobjective clustering technique is able to suitably evolve these cluster centers in such a way so that the two objectives are optimized 'simultaneously'. Assignment of points to different clusters is done based on the newly developed point symmetry based distance rather than the Euclidean distance. Results on eight artificial and six real-life data sets show that the proposed technique is well-suited to detect true partitioning from data sets with clusters having either the hyperspherical shape or point symmetric structure. Results are compared with those obtained by five existing clustering techniques, one multiobjective clustering technique, MOCK, average linkage clustering algorithm, expectation maximization clustering algorithm, well-known genetic algorithm based K-means clustering technique (GAK-means) and a newly developed genetic algorithm with point symmetry based clustering technique (GAPS).