Title: An ant colony optimisation-based approach for clustering in a data matrix

Authors: Chun-Hung Cheng; Angappa Gunasekaran; Kwan-Ho Woo

Addresses: Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Rmbo 7 William MW Mong Engineering Building, Shatin, NT, Hong Kong ' Department of Decision and Information Sciences, University of Massachusetts Dartmouth, North Dartmouth, MA 02747, USA ' XML Asia Ltd., Room 1601, 16/F., Jubilee Centre, 18 Fenwick Street, Wan Chai, Hong Kong

Abstract: Clustering is a process of classifying similar objects into different groups, such that the data within the same groups share the common features. As a common technique in statistical data analysis, it has been addressed in different contexts and by researchers in different disciplines. In this paper, we study the problem of clustering data into a diagonal block structure in a data matrix. This kind of clustering is very useful for analysing the interaction between the objects and their associated attributes in a dataset. In this work, we explore the use of ant colony optimisation-based approach to perform data clustering. Our approach offers several advantages. First, the objects and their attributes are re-arranged in the matrix such that a diagonal block structure is formed. This is useful for visual analysis. Second, our approach can deal with the case when the objects and attributes have weighting associated with them. Third, our approach is a non-parametric clustering method, (i.e., no explicit clustering criterion is required). Our computational study demonstrates the performance of our approach in data clustering.

Keywords: ant colony optimisation; ACO; data matrix; block clustering; data partitioning; diagonal block structure; data clustering.

DOI: 10.1504/IJOR.2014.060413

International Journal of Operational Research, 2014 Vol.19 No.4, pp.407 - 434

Published online: 17 Jun 2014 *

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