Weighted Cluster Ensemble Using a Kernel Consensus Function
Cluster ensemble is a good alternative to face the problem of data clustering. Some studies based on mathematical models have shown that cluster ensemble methods lead to an effective improvement of the results of the standard clustering algorithms. In this paper, we focus on this problem, proposing a new approach to solve it, by adding a new step into the usual cluster ensemble methodology. Representing partitions by graphs and a new kernel function to measure the similarity between partitions are other proposals for this work. Experiments with synthetic and real databases show the suitability and effectiveness of our method.
Keywordscluster ensemble graph kernel consensus function
- 3.Fred, A., Jain, A.K.: Data Clustering Using Evidence Accumulation. In: Proc. of the 16th Intel Conference on Pattern Recognition, ICPR 2002, pp. 276–280. Quebec City (2002)Google Scholar
- 7.Topchy, A.P., Law, M.H.C., Jain, A.K., Fred, A.L.: Analysis of Consensus Partition in Cluster Ensemble. In: International Conference on Data Mining, pp. 225–232 (2006)Google Scholar
- 8.Kuncheva, L.I., Hadjitodorov, S.T., Todorova, L.P.: Experimental Comparison of Cluster Ensemble Methods. In: 9th International Conference on Information Fusion, pp. 1–7 (2006)Google Scholar
- 9.Gorder, A., Filkov, V.: Consensus Clustering: Comparison and Refinement. In: Proceedings of ALENEX, pp. 109–117 (2008)Google Scholar
- 10.Kashima, H., Tsuda, K., Inokuchi, A.: Marginalized Kernels Between Labeled Graphs. In: Proc. of the Twentieth Int. Conf. on Machine Learning (2003)Google Scholar