Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: 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: filippo.binachi@ryerson.ca (Filippo Maria Bianchi), enrico.maiorino@uniroma1.it (Enrico Maiorino), llivi@scs.ryerson.ca (Lorenzo Livi), antonello.rizzi@uniroma1.it (Antonello Rizzi), asadeghi@ryerson.ca (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.

An Agent-Based Algorithm exploiting Multiple Local Dissimilarities for Clusters Mining and Knowledge Discovery