  In this paper we introduce a new ant-based method that takes advantage of the
cooperative self-organization of Ant Colony Systems to create a naturally
inspired clustering and pattern recognition method. The approach considers each
data item as an ant, which moves inside a grid changing the cells it goes
through, in a fashion similar to Kohonen's Self-Organizing Maps. The resulting
algorithm is conceptually more simple, takes less free parameters than other
ant-based clustering algorithms, and, after some parameter tuning, yields very
good results on some benchmark problems.
