Summary
This study focuses on bringing two rough-set-based clustering algorithms into the framework of partially supervised clustering. A mechanism of partial supervision relying on either fuzzy membership grades or rough memberships and non-memberships of patterns to clusters is envisioned. Allowing such knowledge-based hints to play an active role in the discovery of the overall structure of the dataset has proved to be highly beneficial, this being corroborated by the empirical results. Other existing rough clustering techniques can successfully incorporate this type of auxiliary information with little computational effort.
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References
Jain, A.K., Murty, M.N., Flynn, P.J.: ACM Computing Surveys 31(3), 264–323 (1999)
Sato-Ilic, M., Jain, L.: Introduction to fuzzy clustering. In: Sato-Ilic, M., Jain, L. (eds.). Studies in Fuzziness and Soft Computing, vol. 205. Springer, Heidelberg (2006)
Pedrycz, W., Waletzky, J.: Fuzzy Clustering with Partial Supervision. IEEE Trans. on Systems, Man and Cybernetics Part B 27(5), 787–795 (1997)
Pawlak, Z.: Rough sets: theoretical aspects of reasoning about data. Kluwer, Dordrecht (1991)
Pawlak, Z.: Rough sets. International Journal of Information and Computer Sciences 11, 145–172 (1982)
Lingras, P., West, C.: Interval set clustering of Web users with rough k-means. Technical Report No. 2002-002, Saint Mary University, Halifax, Canada (2002)
Lingras, P.: Unsupervised rough set classification using GAs. Journal of Intelligent Information Systems 16(3), 215–228 (2001)
Lingras, P., Yao, Y.: Time complexity of rough clustering: GAs versus K-means. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS, vol. 2475, p. 263. Springer, Heidelberg (2002)
Lingras, P.: Rough set clustering for web mining. In: Proc. of 2002 IEEE International Conference on Fuzzy Systems (2002)
Mitra, S., Banka, H., Pedrycz, W.: Rough-Fuzzy Collaborative Clustering. IEEE Trans. on Systems, Man and Cybernetics 36(4), 795–805 (2006)
Pedrycz, W.: Knowledge-based clustering: from data to information granules. John Wiley & Sons, New York (2005)
Pedrycz, W.: Algorithms of fuzzy clustering with partial supervision. Pattern Recognition Letters 3, 13–20 (1985)
Kersten, P.R.: Including auxiliary information in fuzzy clustering. In: Proc. Biennial Conf. of the North American Fuzzy Information Processing Society, NAFIPS, pp. 221–224 (1996)
Bensaid, A.M., Hall, L.O., Bezdek, J.C., Clarke, L.P.: Partially supervised clustering for image segmentation. Pattern Recognition 29(5), 859–871 (1996)
Liu, H., Huang, S.T.: Evolutionary semi-supervised fuzzy clustering. Pattern Recognition Letters 24(16), 3105–3113 (2003)
Timm, H., Klawonn, F., Kruse, R.: An extension of partially supervised fuzzy cluster analysis. In: Proc. of the Annual Meeting of the North American Fuzzy Information Processing Society, pp. 63–68 (2002)
Pedrycz, W., Waletzky, J.: Fuzzy clustering with partial supervision. IEEE Trans. on Systems, Man and Cybernetics 5, 787–795 (1997)
Abonyi, J., Szeifert, F.: Supervised fuzzy clustering for the identification of fuzzy classifiers. Pattern Recognition Letters 24(14), 2195–2207 (2003)
Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York (1981)
Yao, Y.Y., Li, X., Lin, T.Y., Liu, Q.: Representation and classification of rough set models. In: Proc. of Third International Workshop on Rough Sets and Soft Computing, pp. 630–637 (1994)
Skowron, A., Stepaniuk, J.: Information granules in distributed environment. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS, vol. 1711, pp. 357–366. Springer, Heidelberg (1999)
Buckles, B.P., Petry, F.E.: Genetic Algorithms. IEEE Computer Press, Los Alamitos (1994)
Wall, M.: GALib, a C++ library of genetic components (1993), http://lancet.mit.edu/ga/
Sharma, S.C., Werner, A.: Improved method of grouping provincewide permanent traffic counters. Transportation Research Record 815, Transportation Research Board 13–18 (1981)
Bezdek, J.C., Pal, N.R.: Some new indices for cluster validity. IEEE Trans. on Systems, Man and Cybernetics B 28(3), 301–315 (1998)
Lingras, P., Huang, X.: Statistical, Evolutionary and Neurocomputing Clustering Techniques: Cluster-Based vs Object-Based Approaches. Artificial Intelligence Review 23, 3–29 (2005)
Mitra, S.: An evolutionary rough partitive algorithm. Pattern Recognition Letters 25(12), 1439–1449 (2004)
Lingras, P., Yan, R., West, C.: Comparison of Conventional and Rough K-Means Clustering. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS, vol. 2639. Springer, Heidelberg (2003)
Peters, G.: Some refinements of rough k-means clustering. Pattern Recognition Letters 39, 1481–1491 (2006)
Murphy, P.M., Aha, D.W.: Repository of Machine Learning Databases, http://www.ics.uci.edu/~mlearn/MLRepository.html
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Falcón, R., Jeon, G., Bello, R., Jeong, J. (2009). Rough Clustering with Partial Supervision. In: Abraham, A., Falcón, R., Bello, R. (eds) Rough Set Theory: A True Landmark in Data Analysis. Studies in Computational Intelligence, vol 174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89921-1_5
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DOI: https://doi.org/10.1007/978-3-540-89921-1_5
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