Ricerc@Sapienza

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. During its lifetime, each agent evaluates different parameter con?gurations and takes decisions autonomously for one cluster at a time. Results show that the algorithm is able to discover parameter con?gurations 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 speci?c clustering problems. Notably, we compare our method with respect to several graph-based clustering algorithms and a well-known subspace search method.