Abstract
Data clustering is a difficult and challenging task, especially when the hidden clusters are of different shapes and non-linearly separable in the input space. This paper addresses this problem by proposing a new method that combines a path-based dissimilarity measure and multi-dimensional scaling to effectively identify these complex separable structures. We show that our algorithm is able to identify clearly separable clusters of any shape or structure. Thus showing that our algorithm produces model clusters; that follow the definition of a cluster.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Jain, A., Law, M.: Data Clustering: A User’s Dilemma. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PReMI 2005. LNCS, vol. 3776, pp. 1–10. Springer, Heidelberg (2005)
Jain, A.: Data Clustering: 50 Years Beyond K-means. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part I. LNCS (LNAI), vol. 5211, pp. 3–4. Springer, Heidelberg (2008)
Dhillon, I.S., Guan, Y., Kulis, B.: Kernel k-means: spectral clustering and normalized cuts. In: 10th Int. Conf. on Knowledge Discovery and Data Mining, pp. 551–556. ACM, New York (2004)
Scholkopf, B., Smola, A., Muller, K.: Nonlinear component analysis as a kernel eigenvalue problem. J. Neu. Com. 10, 1299–1319 (1998)
Von Luxburg, U.: A tutorial on spectral clustering. J. Sta. and Com. 17, 395–416 (2007)
Meila, M., Shi, J.: A random walks view of spectral segmentation. In: International Conference on AI and Statistics (2001)
Ng, A., Jordan, M., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: Adv. in Neu. Inf. Pro. Sys. 14, vol. 2, pp. 849–856 (2001)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence 22, 888–905 (2000)
Fischer, B., Zoller, T., Buhmann, J.: Path based pairwise data clustering with application to texture segmentation. In: Figueiredo, M., Zerubia, J., Jain, A.K. (eds.) EMMCVPR 2001. LNCS, vol. 2134, pp. 235–250. Springer, Heidelberg (2001)
Fischer, B., Buhmann, J.M.: Path-based clustering for grouping of smooth curves and texture segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence 25, 514–519 (2003)
Fischer, B., Buhmann, J.M.: Bagging for path-based clustering. IEEE Trans. on Pattern Analysis and Machine Intelligence 25, 1411–1415 (2003)
Chang, H., Yeung, D.: Robust path-based spectral clustering. J. Pat. Rec. 41, 191–203 (2008)
Borg, I., Groenen, P.: Modern multidimensional scaling: Theory and applications. Springer, Heidelberg (2005)
Cormen, T.H., Leiserson, C.E., Rivest, R.L.: Introduction to Algorithms. MIT Press, Cambridge (1989)
Zelnik-Manor, L., Perona, P.: Self-tuning spectral clustering. J. Adv. in Neu. Inf. Pro. Sys. 17, 1601–1608 (2004)
UCI Machine Learning Repository, http://www.ics.uci.edu/~mlearn/MLRepository.html
Cai, D., He, X., Han, J.: Document clustering using locality preserving indexing. IEEE Trans. on Knowledge and Data Engineering 17, 1624–1637 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nguyen, U.T.V., Park, L.A.F., Wang, L., Ramamohanarao, K. (2009). A Novel Path-Based Clustering Algorithm Using Multi-dimensional Scaling. In: Nicholson, A., Li, X. (eds) AI 2009: Advances in Artificial Intelligence. AI 2009. Lecture Notes in Computer Science(), vol 5866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10439-8_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-10439-8_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10438-1
Online ISBN: 978-3-642-10439-8
eBook Packages: Computer ScienceComputer Science (R0)