Clustering ensemble extraction: a knowledge reuse framework

Abstract

Clustering ensemble combines several fundamental clusterings with a consensus function to produce the final clustering without gaining access to data features. The quality and diversity of a vast library of base clusterings influence the performance of the consensus function. When a huge library of various clusterings is not available, this function produces results of lower quality than those of the basic clustering. The expansion of diverse clusters in the collection to increase the performance of consensus, especially in cases where there is no access to specific data features or assumptions in the data distribution, has still remained an open problem. The approach proposed in this paper, Clustering Ensemble Extraction, considers the similarity criterion at the cluster level and places the most similar clusters in the same group. Then, it extracts new clusters with the help of the Extracting Clusters Algorithm. Finally, two new consensus functions, namely Cluster-based extracted partitioning algorithm and Meta-cluster extracted algorithm, are defined and then applied to new clusters in order to create a high-quality clustering. The results of the empirical experiments conducted in this study showed that the new consensus function obtained by our proposed method outperformed the methods previously proposed in the literature regarding the clustering quality and efficiency.