Indexed by:期刊论文

Date of Publication:2008-12-01

Journal:Journal of Computational Information Systems

Included Journals:EI、Scopus

Volume:4

Issue:6

Page Number:2841-2847

ISSN No.:15539105

Abstract:In this paper, we present an agglomerative hierarchical algorithm based on the definition of cluster similarity (CS). Inspired by the node similarity of social networks, we give the definition of CS based on the common connecting strength. Because CS represents the similarity between the clusters by considering the inner and outer structure, the algorithm may obtain nonlocal results. Experiments on the public textual dataset indicate that the modularity, F-measure and entropy obtained by our algorithm are better than the one of FN algorithm and UPGMA. The compute complexity of out algorithm is O (mn ), and it may be suitable for analyzing large or dynamical data. © 2008 Binary Information Press.