External Indices for Rough Clustering

Abstract

Clustering external indices are used to compare the clustering result with a given gold standard, represented (in the classical case) by a partition of the dataset. Rough clustering on the other hand splits the dataset in subsets with uncertain boundaries such that different clusters may overlap, i.e., the result is a covering instead of a partition. The aim of this work is to extend the aforementioned external indices to the rough clustering case, in order to evaluate the results of the clustering with respect to the gold standard. Thus, the comparison of different rough clustering methods among them and with other methods will then be possible.