A major issue in clustering uncertain objects is related to the poor efficiency of existing algorithms, which is mainly due to expensive computation of the distance between uncertain objects. This paper discusses how we addressed this issue through an original formulation of the problem of clustering uncertain objects based on the minimization of the variance of the mixture models that represent the clusters to be discovered. The proposed partitional clustering method, named MMVar, features high efficiency since it does not need to employ any distance measure for uncertain objects. Experiments have shown that MMVar turned out to be faster than prominent state-of-the-art algorithms for clustering uncertain objects, while achieving better average accuracy in terms of both external and internal cluster validity criteria.
|Titolo:||MMVar: Clustering uncertain objects via minimization of the variance of cluster mixture models|
|Data di pubblicazione:||2013|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|