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PRICAI
2004
Springer

Constrained Ant Colony Optimization for Data Clustering

10 years 4 months ago
Constrained Ant Colony Optimization for Data Clustering
Processes that simulate natural phenomena have successfully been applied to a number of problems for which no simple mathematical solution is known or is practicable. Such meta-heuristic algorithms include genetic algorithms, particle swarm optimization and ant colony systems and have received increasing attention in recent years. This paper extends ant colony systems and discusses a novel data clustering process using Constrained Ant Colony Optimization (CACO). The CACO algorithm extends the Ant Colony Optimization algorithm by accommodating a quadratic distance metric, the Sum of K Nearest Neighbor Distances (SKNND) metric, constrained addition of pheromone and a shrinking range strategy to improve data clustering. We show that the CACO algorithm can resolve the problems of clusters with arbitrary shapes, clusters with outliers and bridges between clusters.
Shu-Chuan Chu, John F. Roddick, Che-Jen Su, Jeng-S
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where PRICAI
Authors Shu-Chuan Chu, John F. Roddick, Che-Jen Su, Jeng-Shyang Pan
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