A NEW FEATURE WEIGHTED FUZZY C-MEANS CLUSTERING ALGORITHM

In the field of cluster analysis, most of existing algorithms assume that each feature of the samples plays a uniform contribution for cluster analysis. Considering different features with different importance, feature-weight assignment can be regarded as a special case of feature selection. That is, the feature assigned a value in the interval [0, 1] indicating the importance of that feature, we call this value "feature-weight". In this paper we propose a new feature weighted fuzzy c-means clustering algorithm in a way which this algorithm be able to obtain the importance of each feature, and then use it in appropriate assignment of feature-weight. These weights incorporated into the distance measure to shape clusters based on variability, correlation and weighted features.

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