Dynamic hierarchical algorithms for document clustering

Abstract

In this paper, two clustering algorithms called dynamic hierarchical compact and dynamic hierarchical star are presented. Both methods aim to construct a cluster hierarchy, dealing with dynamic data sets. The first creates disjoint hierarchies of clusters, while the second obtains overlapped hierarchies. The experimental results on several benchmark text collections show that these methods not only are suitable for producing hierarchical clustering solutions in dynamic environments effectively and efficiently, but also offer hierarchies easier to browse than traditional algorithms. Therefore, we advocate its use for tasks that require dynamic clustering, such as information organization, creation of document taxonomies and hierarchical topic detection.