Abstract
In this paper, we present an experimental study on applying a new dissimilarity measure to the k-modes clustering algorithm to improve its clustering accuracy. The measure is based on the idea that the similarity between a data object and cluster mode, is directly proportional to the sum of relative frequencies of the common values in mode. Experimental results on real life datasets show that, the modified algorithm is superior to the original k-modes algorithm with respect to clustering accuracy.
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References
Huang, Z.: Extensions To The K-means Algorithm for Clustering Large Data Sets with Categorical Values. Data Mining and Knowledge Discovery 2, 283–304 (1998)
Huang, Z., Ng, M.K.: A Fuzzy K-modes Algorithm for Clustering Categorical Data. IEEE Transactions on Fuzzy Systems 7(4), 446–452 (1999)
He, Z., Xu, X., Deng, S.: Squeezer: An Efficient Algorithm for Clustering Categorical Data. Journal of Computer Science & Technology 17(5), 611–624 (2002)
He, Z., Xu, X., Deng, S.: A Cluster Ensemble Method for Clustering Categorical Data. Information Fusion 6(2), 143–151 (2005)
Merz, C.J., Merphy, P.: UCI Repository of Machine Learning Databases (1996), http://www.ics.uci.edu/~mlearn/MLRRepository.html
He, Z., Xu, X., Deng, S.: Discovering Cluster-based Local Outliers. Pattern Recognition Letters 24, 1641–1650 (2003)
He, Z., Xu, X., Huang, J.Z., Deng, S.: Mining Class Outliers: Concepts, Algorithms and Applications in CRM. Expert Systems with Applications 27(4), 681–697 (2004)
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© 2005 Springer-Verlag Berlin Heidelberg
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He, Z., Deng, S., Xu, X. (2005). Improving K-Modes Algorithm Considering Frequencies of Attribute Values in Mode. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596448_23
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DOI: https://doi.org/10.1007/11596448_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30818-8
Online ISBN: 978-3-540-31599-5
eBook Packages: Computer ScienceComputer Science (R0)