Rough Inclusion Functions and Similarity Indices

Abstract

Rough inclusion functions are mappings considered in rough set theory with which one can measure the degree of inclusion of a set (information granule) in a set (information granule) in line with rough mereology. On the other hand, similarity indices are mappings used in cluster analysis with which one can compare clusterings, and clustering methods with respect to similarity. In this article we show that a large number of similarity indices, known from the literature, can be generated by three simple rough inclusion functions, the standard rough inclusion function included.