Abstract
In the field of cluster analysis, the fuzzy k-means, k-modes and k-prototypes algorithms were designed for numerical, categorical and mixed data sets respectively. However, all the above algorithms assume that each feature of the samples plays an uniform contribution for cluster analysis. To consider the particular contributions of different features, a novel feature weighted fuzzy clustering algorithm is proposed in this paper, in which the ReliefF algorithm is used to assign the weights for every feature. By weighting the features of samples, the above three clustering algorithms can be unified, and better classification results can be also achieved. The experimental results with various real data sets illustrate the effectiveness of the proposed algorithm.
This work was supported by National Natural Science Foundation of China (No.60202004) and the Key Project of Chinese Ministry of Education (No.104173).
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Li, J., Gao, X., Jiao, L. (2005). A New Feature Weighted Fuzzy Clustering Algorithm. In: Ślęzak, D., Wang, G., Szczuka, M., Düntsch, I., Yao, Y. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3641. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548669_43
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DOI: https://doi.org/10.1007/11548669_43
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