An integer-coded evolutionary approach for mixture maximum likelihood clustering

Authors:
Mohamad M. Tawfick;Hazem M. Abbas;Hussein I. Shahein
Affiliations:
Mentor Graphics Inc., 51 Beirut Street, Helipolis, Cairo 11341, Egypt;Ain Shams University, Department of Computer and Systems Engineering, Abbasia, Cairo 11571, Egypt;Ain Shams University, Department of Computer and Systems Engineering, Abbasia, Cairo 11571, Egypt
Venue:
Pattern Recognition Letters
Year:
2008

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Abstract

This paper outlines an algorithm for solving the maximum mixture likelihood clustering problem using an integer-coded genetic algorithm (IGA-ML) where a fixed length chromosome encodes the object-to-cluster assignment. The main advantage of the outlined algorithm (IGA-ML) compared with other known algorithms, such as the k-means technique, is that it can successfully discover the correct number of clusters, in addition to carrying out the partitioning process. The algorithm implements a post-fixing sorting mechanism that drastically reduces the searched solution space by eliminating duplicate solutions that appear after applying the genetic operations. Simulation results show the effectiveness of the algorithm especially with the case of overlapping clusters.