A hierarchical clusterer ensemble method based on boosting theory

Bagging and boosting are two successful well-known methods for developing classifier ensembles. It is recognized that the clusterer ensemble methods which utilize the boosting concept, can create clusterings with quality and robustness improvement. In this paper, we introduce a new boosting based hierarchical clusterer ensemble method called Bob-Hic. This method is utilized to create a consensus hierarchical clustering (h-clustering) on a dataset which is helpful to improve the clustering accuracy. Bob-Hic includes several boosting iterations, and in each iteration, first a weighted random sampling is performed on the original dataset and then an individual h-clustering is created on the selected samples. At the end of the iterations, the individual clusterings are combined to a final consensus hclustering. The middle structures used in the combination are distance descriptor matrices which correspond to individual h-clustering results. This final integration is done through an information theoretic approach. Experiments on both synthetic and real popular datasets confirm that the proposed method improves the results of simple clustering algorithms. This method provides better consensus clustering quality in comparison to other available ensemble techniques.