Abstract
Clustering Ensemble effectively improves clustering accuracy, stability and robustness, which is most resulted from the diversity of the base clustering results. It is a key point to measure the diversity of clustering results. This paper proposes a method to measure diversity of base clustering results and a covariance-based selective clustering ensemble algorithm. Experiments on 20 UCI data sets show that this algorithm effectively improves the clustering performance.
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References
Han, J.W., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann (2007)
Tang, W., Zhou, Z.H.: Selective Clustering Ensemble Based on Bagging. Journal of Software 16(4), 496–502 (2005)
Strehl, A., Ghosh, J., Cardie, C.: Cluster Ensembles: A Knowledge Reuse Framework for Combining Multiple Partitions. Journal of Machine Learning Research (3), 583–617 (2002)
Topchy, A., Jain, A.K., Punch, W.: A Mixture Model for Clustering Ensembles. In: Proc. of the 4th SIAM International Conference on Data Mining, pp. 379–390 (2004)
Topchy, A., Jain, A.K., Punch, W.: Clustering Ensembles: Models of Consensus and Weak Partitions. IEEE Trans. on Pattern Analysis and Machine Intelligence 21(12), 1866–1881 (2005)
Luo, H.L., Jing, F.R., Xie, X.B.: Combining Multiple Clusterings Using Information Theory Based Genetic Slgorithm. In: IEEE International Conference on Computational Intelligence and Security, vol. 1, pp. 84–89 (2006)
Hassan, S.Z., Verma, B.: Decisions Fusion Strategy: Towards Hybrid Cluster Ensemble. In: Intelligent Sensors, Sensor Networks and Information, pp. 377–382 (2007)
Mohammadi, M., Nikanjam, A., Rahmani, A.: An Evolutionary Approach to Clustering Ensemble. In: The Fourth International Conference on Natural Computation, vol. 3, pp. 77–82 (2008)
Iqbal, A.M., Moh’d, A., Khan, Z.A.: Semi-supervised Clustering Ensemble by Voting. In: The International Conference on Information and Communication System, pp. 1–5 (2009)
Wang, H.J., Li, Z.S., Qi, J.H.: Semi-Supervised Cluster Ensemble Model Based on Bayesian Network. Journal of Software 21(11), 2814–2825 (2010)
Jia, J.H., Xiao, X., Liu, B.X.: Bagging-Based Spectral Clustering Ensemble Selection. Pattern Recognition Letters 32(10), 1456–1467 (2011)
Yang, Y., Wang, H., Lin, C., Zhang, J.: Semi-supervised Clustering Ensemble Based on Multi-ant Colonies Algorithm. In: Li, T., Nguyen, H.S., Wang, G., Grzymala-Busse, J., Janicki, R., Hassanien, A.E., Yu, H. (eds.) RSKT 2012. LNCS (LNAI), vol. 7414, pp. 302–309. Springer, Heidelberg (2012)
Iam-On, N., Boongoen, T., Garrett, S., Price, C.: A Link-Based Cluster Ensemble Approach for Categorical Data Clustering. IEEE Transactions on Knowledge and Data Engineering 24(3), 413–425 (2012)
Zhou, Z.H., Wu, J., Tang, W.: Ensembling Neural Networks: Many Could be Better Than All. Artificial Intelligence 137(1-2), 239–263 (2002)
Fern, X.Z., Lin, W.: Cluster ensemble selection. Statistical Analysis and Data Mining 1(3), 128–141 (2008)
Azimi, J., Fern, X.Z.: Adaptive cluster ensemble selection. In: Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI 2009), pp. 992–997 (2009)
Jia, J.H., Xiao, X., Liu, B.X.: Similarity-based Spectral Clustering Ensemble Selection. In: Proceedings of the 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2012), pp. 1071–1074 (2012)
Liu, L.M., Fan, X.P.: A New Selective Clustering Ensemble Algorithm. In: Proceedings of the 9th International Conference on e-Business Engineering (ICEBE), pp. 45–49 (2012)
Fern, X.Z., Brodley, C.E.: Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach. In: Proceedings of the 20th International Conference on Machine Learning (ICML 2003), pp. 186–193 (2003)
Derek, G., Alexey, T., Nadia, B.: Ensemble Clustering in Medical Diagnostics. In: Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems, CBMS, pp. 576–581 (2004)
Hadjitodorov, S.T., Kuncheve, L.I., Todorova, L.P.: Moderate Diversity for Better Cluster Ensembles. Information Fusion 7(3), 264–275 (2006)
Luo, H.L., Kong, F.S., Li, Y.X.: Diversity Measure of Cluster Ensemble. Journal of Computers 30(8), 1315–1324 (2007)
Li, K., Gao, H.T.: A Novel Measure of Diversity for Support Vector Machine Ensemble. In: Proceedings of the Third International Symposium on Intelligent Information Technology and Security Informatics (IITSI), pp. 367–370 (2010)
Zhou, Z.H.: Ensemble Methods: Foundations and Algorithms, Boca Raton, FL (2012)
MacQueen, J.: Some Methods for Classification and Analysis of Multivariate Observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)
Hartigan, J.A., Wong, M.A.: Algorithm AS 136: A K-Means Clustering Algorithm. Applied Statistics 28(1), 100–108 (1979)
Frey, J.B., Dueck, D.: Clustering by Passing Messages Between Data Points. Science 315(5814), 972–976 (2007)
Dunn, J.C.: A Graph Theoretic Analysis of Pattern Classification via Tamura’s Fuzzy Relation. Journal of Cybernetics 3(3), 32–57 (1973)
Bezdek, J.C.: A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 2(1), 1–8 (1980)
Yang, Y., Kamel, M.: An Aggregated Clustering Approach Using Multi-Ant Colonies Algorithms. Pattern Recognition 39(7), 1278–1289 (2006)
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Lu, X., Yang, Y., Wang, H. (2013). Selective Clustering Ensemble Based on Covariance. In: Zhou, ZH., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2013. Lecture Notes in Computer Science, vol 7872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38067-9_16
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DOI: https://doi.org/10.1007/978-3-642-38067-9_16
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