AUTOMATIC GENERATION OF FUZZY MEMBERSHIP FUNCTIONS BASED ON K-MEANS AND EM CLUSTERING

I. MALA, P. AKHTAR, A. R. MEMON, T. J. ALI

Abstract


Fuzzy partitioning is based on expert opinion in generating fuzzy membership function. Automatic generation of fuzzy partitioning can be made for a given dataset, using different clustering algorithms. We have developed a fuzzy partitioning algorithm, based on clustering algorithm. For medical dataset, automatic generation of fuzzy membership function is done using K-Means and Expected Maximization (EM) clustering algorithms. According to our results, K-Means clustering is more accurate for fuzzy partitioning as compared to EM clustering based on our developed fuzzy partitioning algorithm.

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