Abstract
With the advance of microarray technology, clustering analysis has become a key tool to make sense of the massive amounts of genes expression data. An artificial ants sleeping model (ASM) and an adaptive artificial ants clustering algorithm (A/sup 4/C) are presented to solve the clustering problem in data mining by simulating the behaviors of social ant colonies. In the ASM model, each datum is represented by an agent. The agents' environment is a two-dimensional grid. In A/sup 4/C, the agents can form into high-quality clusters by making simple moves according to little local information from its neighborhood and the parameters are selected and adjusted adaptively. Experimental results on clustering benchmarks show the ASM and A/sup 4/C are simpler, easier to implement, and more efficient than previous methods.
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