Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
A new feature weighted fuzzy clustering algorithm
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
A fuzzy k-modes algorithm for clustering categorical data
IEEE Transactions on Fuzzy Systems
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In the field of cluster analysis, most of existing algorithms assume that each feature of the samples plays a uniform contribution for cluster analysis and they are mainly developed for the uniform distribution of sample with numerical or categorical data, which cannot effectively process the non-uniformly distribution with mixed data sets in data mining. For this purpose, we propose a new general Weighted Fuzzy Clustering Algorithm to deal with the mixed data including different sample distributions and different features, in which the idea of the probability density of samples is used to assign the weights to each sample and the ReliefF algorithms is applied to give the weights to each feature. By weighting the samples and their features, the fuzzy c-means, fuzzy c-modes, fuzzy c-prototype and sample-weighted algorithms can be unified into a general framework. The experimental results with various test data sets illustrate the effectiveness of the proposed clustering algorithm.