Abstract
The cluster validity is an important topic of cluster analysis, which is often converted into the determination of the optimal cluster number. Most of the available cluster validity functions are limited for the analysis of numeric data set and ineffective for the categorical data set. For this purpose, a new cluster validity function is presented in this paper, namely the modified partition fuzzy degree. By combining the partition entropy and the partition fuzzy degree, the new cluster validity can be applied to any data set with numeric attributes or categorical attributes. The experimental results illustrate the effectiveness of the proposed cluster validity function.
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References
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
Xinbo, G., Weixin, X.: Advances in theory and applications of fuzzy clustering. Chinese Science Bulletin 45(11), 961–970 (2000)
Huang, Z., Michael, K.N.: A Fuzzy k-modes Algorithm for clustering categorical Data. IEEE Trans. on Fuzzy Systems 7(4), 446–452 (1999)
Sudipto, G., Rajeev, R., Kyuseok, S.: ROCK: A Robust Clustering Algorithm for Categorical Attributes. In: Proceedings of the IEEE International Conference on Data Engineering, Sydney (March 1999)
Yang, Y., Guan, X., You, J.: CLOPE: A Fast and Effective Clustering Algorithm for Transactional Data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada (July 2002)
Michalski, R.S., Stepp, R.E.: Automated construction of classifications: Conceptual clustering versus numerical taxonomy. IEEE Trans. on PAMI 5, 396–410 (1983)
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© 2004 Springer-Verlag Berlin Heidelberg
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Li, J., Gao, X., Jiao, Lc. (2004). A New Cluster Validity Function Based on the Modified Partition Fuzzy Degree. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_72
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DOI: https://doi.org/10.1007/978-3-540-25929-9_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22117-3
Online ISBN: 978-3-540-25929-9
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