Abstract
A fundamental limitation of the data clustering task is that it has an inherent, ill-defined model selection problem: the choice of a clustering technique also implies some a-priori decision on cluster geometry. In this work we explore the combined use of two different clustering paradigms and their combination by means of an ensemble technique. Mixing coefficients are computed on the basis of partition quality, so that the ensemble is automatically tuned so as to give more weight to the best-performing (in terms of the selected quality indices) clustering method.
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References
Asuncion, A., Newman, D.J.: UCI machine learning repository (2007)
Bach, F.R., Jordan, M.I.: Learning spectral clustering. Tech. Rep. UCB/CSD-03-1249, EECS Department, University of California, Berkeley (2003)
Baraldi, A., Blonda, P.: A survey of fuzzy clustering algorithms for pattern recognition. I. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 29, 778–785 (1999)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell (1981)
Chaudhuri, K., Chung, F., Tsiatas, A.: Spectral clustering of graphs with general degrees in the extended planted partition model. Journal of Machine Learning Research 2012, 1–23 (2012)
Chung, F.R.K.: Spectral Graph Theory. CBMS Regional Conference Series in Mathematics, vol. 92. American Mathematical Society (February 1997)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI 1(2), 224–227 (1979)
Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics 3, 32–57 (1974)
Fiedler, M.: Algebraic connectivity of graphs. Czechoslovak Mathematical Journal 23(2), 298–305 (1973)
Filippone, M., Camastra, F., Masulli, F., Rovetta, S.: A survey of kernel and spectral methods for clustering. Pattern Recognition 40(1), 176–190 (2008)
Fischer, I., Poland, J.: New methods for spectral clustering. Tech. rep., IDSIA/USI-SUPSI (2004)
Fred, A.L.N., Jain, A.K.: Data clustering using evidence accumulation. In: International Conference on Pattern Recognition, vol. 4 (2002)
Gower, J.C., Ross, G.J.S.: Minimum spanning trees and single linkage cluster analysis. Journal of the Royal Statistical Society 18(1), 54–64 (1969)
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs (1988)
Kriegel, H.P., Kröger, P., Sander, J., Zimek, A.: Density-based clustering. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1(3), 231–240 (2011)
Krishnapuram, R., Keller, J.M.: A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems 1(2), 98–110 (1993)
Kuncheva, L.: Combining pattern classifiers. Methods and Algorithms. Wiley, Chichester (2004)
Masulli, F., Rovetta, S.: Soft transition from probabilistic to possibilistic fuzzy clustering. IEEE Transactions on Fuzzy Systems 14(4), 516–527 (2006)
Nadler, B., Galun, M.: Fundamental limitations of spectral clustering. In: Advances in Neural Information Processing Systems, vol. 19, p. 1017 (2007)
Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems, vol. 14. MIT Press, Cambridge (2002)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)
Strehl, A., Ghosh, J.: Cluster ensembles — a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2003)
Von Luxburg, U.: A tutorial on spectral clustering. Statistics and Computing 17(4), 395–416 (2007)
Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(8), 841–847 (1991)
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Rosasco, R., Mahmoud, H., Rovetta, S., Masulli, F. (2014). A Quality-Driven Ensemble Approach to Automatic Model Selection in Clustering. In: Bassis, S., Esposito, A., Morabito, F. (eds) Recent Advances of Neural Network Models and Applications. Smart Innovation, Systems and Technologies, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-04129-2_6
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DOI: https://doi.org/10.1007/978-3-319-04129-2_6
Publisher Name: Springer, Cham
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