Abstract
K-means algorithm is a well-known clustering method. Typically, the k-means algorithm treats all features fairly and sets weights of all features equally when evaluating dissimilarity. However, experiment results show that a meaningful clustering phenomenon often occurs in a subspace defined by some specific features. Different dimensions make contributions to the identification of points in a cluster. The contribution of a dimension is represented as a weight that can be treated as the degree of the dimension in contribution to the cluster. This paper first proposes Weight in Competitive K-means (WCKM), which derives from Improved K-means and Entropy Weighting K-means. By adding weights to the objective function, the contributions from each feature of each clustering could simultaneously minimize the dispersion within clusters and maximize the separation between clusters. The proposed algorithm is confirmed by experiments on real data sets.
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References
Fayyad, U.M., et al.: Advances in knowledge discovery and data mining (1996)
Duda, R.O., et al.: Pattern classification, vol. 2. Wiley, New York (2001)
Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recognition Letters 31, 651–666 (2010)
Jain, A.K., Dubes, R.C.: Algorithms for clustering data. Prentice-Hall, Inc. (1988)
Jain, A.K., et al.: Data clustering: a review. ACM Computing Surveys (CSUR) 31, 264–323 (1999)
MacQueen, J.: Some methods for classification and analysis of multivariate observations, p. 14 (1967)
Forgy, E.W.: Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics 21, 768–769 (1965)
Xu, L., et al.: Rival penalized competitive learning for clustering analysis, RBF net, and curve detection. IEEE Transactions on Neural Networks 4, 636–649 (1993)
Zalik, K.R.: An efficient k’-means clustering algorithm. Pattern Recognition Letters 29, 1385–1391 (2008)
Cui, T., Zhang, S.: Improved K-means Algorithm Based on Competitive Learnin. Presented at the 2011 3rd IEEE International Conference on Signal Processing Systems (ICSPS 2011), Yantai, China (2011)
Fang, C., Ma, J.: A novel k’-means algorithm for clustering analysis, pp. 1–5 (2008)
Ma, J., Wang, T.: A cost-function approach to rival penalized competitive learning (RPCL). IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 36, 722–737 (2006)
Agrawal, R., et al.: Automatic subspace clustering of high dimensional data for data mining applications, vol. 27. ACM (1998)
Cheng, C.H., et al.: Entropy-based subspace clustering for mining numerical data, pp. 84–93 (1999)
Goil, S., et al.: MAFIA: Efficient and scalable subspace clustering for very large data sets (2001)
Liu, B., et al.: Clustering through decision tree construction, pp. 20–29 (2000)
Jing, L., et al.: An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data. IEEE Transactions on Knowledge and Data Engineering, 1026–1041 (2007)
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Cui, T., Zhao, X., Wang, Z., Zhang, Y. (2012). Weight in Competitive K-Means Algorithm. In: Sambath, S., Zhu, E. (eds) Frontiers in Computer Education. Advances in Intelligent and Soft Computing, vol 133. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27552-4_140
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DOI: https://doi.org/10.1007/978-3-642-27552-4_140
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27551-7
Online ISBN: 978-3-642-27552-4
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