An effective ensemble method for hierarchical clustering
Mahmood Hossain, Susan M. Bridges, Yong Wang, Julia E. Hodges · 2012
The notion of cluster ensembles has been accepted in recent years as an effective alternative to individual clustering. Ensemble based clustering methods have typically focussed on combining different clustering results from a single data set and have been limited to partitional clustering only. In this paper, we present an effective ensemble algorithm for combining the results of hierarchical clustering of multiple datasets. We use a graph theoretic approach to combine multiple cluster hierarchies into a single set of partitional clusters. A graph is generated from the cluster hierarchies based on the association strengths of objects in the hierarchies. A graph partitioning algorithm is then applied to generate partitional clusters. Our algorithm can handle multiple contextually related heterogeneous datasets that use different feature sets, but consist of non-disjoint sets of objects. We used our algorithm to solve a document clustering problem and experimental results demonstrate the effectiveness of our algorithm.