Algorithm for Hierarchical Multi-way Divisive Clustering of Document Collections
Kazuaki Kishida · 2014
This paper proposes a novel algorithm of hierarchical divisive clustering, which generates a multi-branch tree, not a binary one, as its output. In order to use the algorithm for clustering large document sets, a spherical kmeans clustering algorithm based on a cosine measure is adopted for partitioning recursively the document set from the top to bottom. Also, by selecting automatically the number of clusters in each partitioning according to a criterion, an optimal multi-way branching is determined for each node of the tree. This paper reports an experimental result indicating the effectiveness of the proposed algorithm.