Clustering Ensemble Method for Heterogeneous Partitions
Cluster ensemble is a promising technique for improving the clustering results. An alternative to generate the cluster ensemble is to use different representations of the data and different similarity measures between objects. This way, it is produced a cluster ensemble conformed by heterogeneous partitions obtained with different point of views of the faced problem. This diversity enhances the cluster ensemble but, it restricts the combination process since it makes difficult the use of the original data. In this paper, in order to solve these limitations, we propose a unified representation of the objects taking into account the whole information in the cluster ensemble. This representation allows working with the original data of the problem regardless of the used generation mechanism. Also, this new representation is embedded in the WKF  algorithm making a more robust cluster ensemble method. Experimental results with numerical, categorical and mixed datasets show the accuracy of the proposed method.
KeywordsCluster ensemble object representation similarity measure co-association matrix
- 4.Pekalska, E., Duin, R.P.W.: The Dissimilarity Representation for Pattern Recognition: Foundations And Applications. In: Machine Perception and Artificial Intelligence. World Scientific Publishing Co., Singapore (2005)Google Scholar
- 5.Kuncheva, L., Hadjitodorov, S., Todorova, L.: Experimental comparison of cluster ensemble methods. In: Int. Conference on Information Fusion, pp. 1–7 (2006)Google Scholar
- 8.UCI machine learning repository, http://archive.ics.uci.edu/ml/datasets.html