An Algorithm to Model Paradigm Shifting in Fuzzy Clustering
نویسندگان
چکیده
The graded possibilistic clustering paradigm includes as the two extreme cases the “probabilistic” assumption and the “possibilistic” assumption adopted by many clustering algorithms. We propose an implementation of a graded possibilistic clustering algorithm based on an interval equality constraint enforcing both the normality condition and the required graded possibilistic condition. Experimental results highlight the different properties attainable through appropriate implementation of a suitable graded possibilistic model.
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