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. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Chu, S. C., Roddick, J. F., Su, C. J., & Pan, J. S. (2004). Constrained Ant Colony Optimization for data clustering. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3157, pp. 534–543). Springer Verlag. https://doi.org/10.1007/978-3-540-28633-2_57
Mendeley helps you to discover research relevant for your work.