Generalization of clustering agreements and distances for overlapping clusters and network communities

• Autores: Reihaneh Rabbany, Osmar Zaiane
• Localización: Data mining and knowledge discovery, ISSN 1384-5810, Vol. 29, Nº 5, 2015, págs. 1458-1485
• Idioma: inglés
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• Resumen

  • A measure of distance between two clusterings has important applications, including clustering validation and ensemble clustering. Generally, such distance measure provides navigation through the space of possible clusterings. Mostly used in cluster validation, a normalized clustering distance, a.k.a. agreement measure, compares a given clustering result against the ground-truth clustering. The two widely-used clustering agreement measures are adjusted rand index and normalized mutual information. In this paper, we present a generalized clustering distance from which these two measures can be derived. We then use this generalization to construct new measures specific for comparing (dis)agreement of clusterings in networks, a.k.a. communities. Further, we discuss the difficulty of extending the current, contingency based, formulations to overlapping cases, and present an alternative algebraic formulation for these (dis)agreement measures. Unlike the original measures, the new co-membership based formulation is easily extendable for different cases, including overlapping clusters and clusters of inter-related data. These two extensions are, in particular, important in the context of finding communities in complex networks.