An agent-based algorithm exploiting multiple local dissimilarities for clusters mining and knowledge discovery
We propose a multi-agent algorithm able to automatically discover relevant regularities in a given dataset, determining at the same time the set of con?gurations of the adopted parametric dissimilarity measure that yield compact and separated clusters. Each agent operates independently by performing a Markovian random walk on a weighted graph representation of the input dataset. Such a weighted graph representation is induced by a speci?c parameter con?guration of the dissimilarity measure adopted by an agent for the search.