A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data

A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data

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The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data.In this article, an enhancement of the algorithm is proposed which is Chips suitable for clustering trapezoidal fuzzy data.A linear ranking function is used to define a distance for trapezoidal fuzzy data.

Then, as LINDEN FLOWERS an application, a method based on the proposed algorithm is presented to cluster incomplete fuzzy data.The method substitutes missing attribute by a trapezoidal fuzzy number to be determined by using the corresponding attribute of q nearest-neighbor.Comparisons and analysis of the experimental results demonstrate the capability of the proposed method.