Abstract
Spectral clustering is one of the most popular modern graph clustering techniques in machine learning. By using the eigenvalue analysis, spectral methods partition the given set of points into number of disjoint groups. Spectral methods are very useful in determining non-convex shaped clusters, identifying such clusters is not trivial for many traditional clustering methods including hierarchical and partitional methods. Spectral clustering may be carried out either as recursive bi-partitioning using fiedler vector (second eigenvector) or as muti-way partitioning using first k eigenvectors, where k is the number of clusters. Although spectral methods are widely discussed, there has been a little attention on which post-clustering algorithm (for eg. K-means) should be used in multi-way spectral partitioning. This motivated us to carry out an experimental study on the influence of post-clustering phase in spectral methods. We consider three clustering algorithms namely K-means, average linkage and FCM. Our study shows that the results of multi-way spectral partitioning strongly depends on the post-clustering algorithm.
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Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. (CSUR) 31(3), 264–323 (1999)
Xu, R., Wunsch, D., et al.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678 (2005)
Jain, A.K.: Data Clustering: User’s Dilemma. In: Perner, P. (ed.) MLDM 2007. LNCS (LNAI), vol. 4571, pp. 1–1. Springer, Heidelberg (2007)
Schaeffer, S.E.: Graph clustering. Comput. Sci. Rev. 1(1), 27–64 (2007)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)
Ng, A.Y., Jordan, M.I., Weiss, Y., et al.: On spectral clustering: Analysis and an algorithm. Adv. Neural Inf. Process. Syst. 2, 849–856 (2002)
Verma, D., Meila, M.: A comparison of spectral clustering algorithms. Technical report (2003)
Jordan, F., Bach, F.: Learning spectral clustering. Adv. Neural Inf. Process. Systems 16, 305–312 (2004)
Von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)
Repository, U.M.L. http://archive.ics.uci.edu/ml
Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. J. Intell. Inf. Syst. 17(2), 107–145 (2001)
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Jothi, R., Mohanty, S.K., Ojha, A. (2015). On the Impact of Post-clustering Phase in Multi-way Spectral Partitioning. In: Prasath, R., Vuppala, A., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2015. Lecture Notes in Computer Science(), vol 9468. Springer, Cham. https://doi.org/10.1007/978-3-319-26832-3_16
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DOI: https://doi.org/10.1007/978-3-319-26832-3_16
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