Hierarchical document clustering using frequent closed sets

Abstract

FIHC is a novel data mining algorithm for hierarchical grouping of text documents. It uses frequent term sets to identify clusters. The approach fails when the number of frequent sets of terms is large. In order to overcome this problem, we propose a method for generating the FIHC's hierarchy of clusters by using only frequent closed sets. The new approach is validated on a number of Cluto package datasets. The experiments prove that our version of FIHC is faster up to two orders of magnitude than the original. © 2006 Springer.