Hierarchical Similarity Measure for Spectral Clustering

Lucas Siviero Sibemberg, Luiz Emilio Allem, Carlos Hoppen

Resumo


In this paper we propose a novel similarity measure for spectral clustering that incorporates a hierarchical component. The main advantage of this measure is that it produces an algorithm that does not depend on any scaling parameter, making it very easy to apply. Our experiments showed that our algorithm performs better than other spectral clustering methods on synthetic data sets with complex shape and multiple scales.


Palavras-chave


Data Science; Clustering; Spectral Graph Theory; Spectral Clustering; Similarity Measure.

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Referências


H. Chang and D. Yeung. “Robust path-based spectral clustering”. In: Pattern Recognition 41.1 (2008), pp. 191–203. doi: 10.1016/j.patcog.2007.04.010.

H. Jia, S. Ding, X. Xu, and R. Nie. “The latest research progress on spectral clustering”. In: Neur. Comput. and Appl. 24.7 (2014), pp. 1477–1486. doi: 10.1007/s00521-013-1439-2.

U. von Luxburg. “A Tutorial on Spectral Clustering”. In: Statistics and computing 17.4 (2007), pp. 395–416. doi: 10.1007/s11222-007-9033-z.

J. Macqueen. “Some methods for classification and analysis of multivariate observations”. In: 5-th Berkeley Symposium on Mathematical Statistics and Probability (1967), pp. 281–297.

Andrew Y. Ng, Michael I. Jordan, and Yair Weiss. “On Spectral Clustering: Analysis and an Algorithm”. In: MIT Press, 2001, pp. 849–856. doi: 10.5555/2980539.2980649.

J. Shi and J. Malik. “Normalized cuts and image segmentation”. In: IEEE Trans. Pattern Anal. Machine Intell. 22.8 (2000), pp. 888–905. doi: 10.1109/34.868688.

R. Sibson. “SLINK: An optimally efficient algorithm for the single-link cluster method”. In: The Computer Journal 16.1 (1973), pp. 30–34.

L. Zelnik-Manor and P. Perona. “Self-Tuning Spectral Clustering”. In: Advances in Neural Information Processing Systems (NIPS) 17 (2004). doi: 10.5555/2976040.2976241.

X. Zhang, J. Li, and H. Yu. “Local density adaptive similarity measurement for spectral clustering”. In: Pattern Recognition Letters 32 (2011), pp. 352–358. doi: 10.1016/j. patrec.2010.09.014




DOI: https://doi.org/10.5540/03.2022.009.01.0263

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