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 yielding compact and separated clusters. Each agent operates independently by performing a Markovian random walk on a suitable weighted graph representation of the input dataset. Such a weighted graph representation is induced by the specific parameter configuration of the dissimilarity measure adopted by the agent, which searches and takes decisions autonomously for one cluster at a time. Results show that the algorithm is able to discover parameter configurations Corresponding Author Email addresses: [email protected] (Filippo Maria Bianchi), [email protected] (Enrico Maiorino), [email protected] (Lorenzo Livi), [email protected] (Antonello Rizzi), [email protected] (Alireza Sadeghian) URL: https://sites.google.com/site/lorenzlivi/ (Lorenzo Livi), http://infocom.uniroma1.it/~rizzi/ (Antonello Rizzi), http://www.scs.ryerson.ca/~asadeghi/ (Alireza Sadeghian) Preprint submitted to Information Sciences September 18, 2014 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.