Abstract
With the gaining popularity of rough clustering, soft computing research community is studying relationships between rough and fuzzy clustering as well as their relative advantages. Both rough and fuzzy clustering are less restrictive than conventional clustering. Fuzzy clustering memberships are more descriptive than rough clustering. In some cases, descriptive fuzzy clustering may be advantageous, while in other cases it may lead to information overload. This paper provides an experimental comparison of both the clustering techniques and describes a procedure for conversion from fuzzy membership clustering to rough clustering. However, such a conversion is not always necessary, especially if one only needs lower and upper approximations. Experiments also show that descriptive fuzzy clustering may not always (particularly for high dimensional objects) produce results that are as accurate as direct application of rough clustering. We present analysis of the results from both the techniques.
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References
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Bezdek, J.C., Hathaway, R.J.: Optimization of Fuzzy Clustering Criteria using Genetic Algorithms (1994)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algoritms. Plenum Press, New York (1981)
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 (1973)
Hartigan, J.A., Wong, M.A.: Algorithm AS136: A K-Means Clustering Algorithm. Applied Statistics 28, 100–108 (1979)
Ho, T.B., Nguyen, N.B.: Nonhierarchical Document Clustering by a Tolerance Rough Set Model. International Journal of Intelligent Systems 17, 199–212 (2002)
Joshi, M., Lingras, P.: Evolutionary and Iterative Crisp and Rough Clustering I: Theory. In: Chaudhury, S., Mitra, S., Murthy, C.A., Sastry, P.S., Pal, S.K. (eds.) PReMI 2009. LNCS, vol. 5909, pp. 615–620. Springer, Heidelberg (2009)
Joshi, M., Lingras, P.: Evolutionary and Iterative Crisp and Rough Clustering II: Experiments. In: Chaudhury, S., Mitra, S., Murthy, C.A., Sastry, P.S., Pal, S.K. (eds.) PReMI 2009. LNCS, vol. 5909, pp. 621–627. Springer, Heidelberg (2009)
Lingras, P., West, C.: Interval Set Clustering of Web Users with Rough K-Means. Journal of Intelligent Information Systems 23, 5–16 (2004)
Lingras, P., Hogo, M., Snorek, M.: Interval Set Clustering of Web Users using Modified Kohonen Self-Organizing Maps based on the Properties of Rough Sets. Web Intelligence and Agent Systems: An International Journal 2(3), 217–230 (2004)
Lingras, P.: Precision of rough set clustering. In: Chan, C.-C., Grzymala-Busse, J.W., Ziarko, W.P. (eds.) RSCTC 2008. LNCS (LNAI), vol. 5306, pp. 369–378. Springer, Heidelberg (2008)
Lingras, P., Chen, M., Miao, D.: Rough Multi-category Decision Theoretic Framework. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds.) RSKT 2008. LNCS (LNAI), vol. 5009, pp. 676–683. Springer, Heidelberg (2008)
Lingras, P.: Evolutionary rough K-means Algorithm. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds.) RSKT 2009. LNCS, vol. 5589, pp. 68–75. Springer, Heidelberg (2009)
Pedrycz, W., Waletzky, J.: Fuzzy Clustering with Partial Supervision. IEEE Trans. on Systems, Man and Cybernetics 27(5), 787–795 (1997)
Peters, G.: Some Refinements of Rough k-Means. Pattern Recognition 39, 1481–1491 (2006)
Peters, J.F., Skowron, A., Suraj, Z., et al.: Clustering: A Rough Set Approach to Constructing Information Granules. Soft Computing and Distributed Processing, 57–61 (2002)
MacQueen, J.: Some Methods for Classification and Analysis of Multivariate Observations. In: Proceedings of Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)
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Joshi, M., Lingras, P., Rao, C.R. (2010). Analysis of Rough and Fuzzy Clustering. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds) Rough Set and Knowledge Technology. RSKT 2010. Lecture Notes in Computer Science(), vol 6401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16248-0_92
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DOI: https://doi.org/10.1007/978-3-642-16248-0_92
Publisher Name: Springer, Berlin, Heidelberg
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