Abstract
An artificial ant sleeping model (ASM) and adaptive artificial ants clustering algorithm (A /sup 4/C) are presented to resolve the clustering problem in data mining by simulating the behaviors of gregarious ant colonies. In the ASM mode, each data is represented by an agent. The agents' environment is a two-dimensional grid. In A /sup 4/C, the agents can be formed into high-quality clusters by making simple move according to little local neighborhood information and the parameters are selected and adjusted adaptively. Experimental results on standard clustering benchmarks demonstrate the ASM and A /sup 4/C are more direct, easy to implement, and more efficient than previous methods.
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