Abstract
In view of local feature weighting hard C-means (LWHCM) clustering algorithm sensitive to noise, based on a non-Euclidean metric, a robust local feature weighting hard C-means (RLWHCM) clustering algorithm is presented. The robustness of RLWHCM is analyzed by using the location M-estimate in robust statistical theory. By endowing each data point with a dynamic weighting function on each feature of data point, RLWHCM can estimate the clustering center more accurately in noisy environment. Experimental results on synthetic and real world data sets demonstrate the advantages of RLWHCM over LWHCM.
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© 2012 Springer-Verlag Berlin Heidelberg
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Zhi, X., Fan, J., Zhao, F. (2012). Robust Local Feature Weighting Hard C-Means Clustering Algorithm. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_75
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DOI: https://doi.org/10.1007/978-3-642-31919-8_75
Publisher Name: Springer, Berlin, Heidelberg
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