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 configurations 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 specific parameter configuration of the dissimilarity measure adopted by an agent for the search. During its lifetime, each agent evaluates different parameter configurations and takes decisions autonomously for one cluster at a time. Results show that the algorithm is able to discover parameter configurations that yield a consistent and interpretable collection of clusters. Moreover, we demonstrate that our algorithm shows comparable performances with other similar state-of-the-art algorithms when facing specific clustering problems. Notably, we compare our method with respect to several graph-based clustering algorithms and a well-known subspace search method.