Generalized Fuzzy Clustering Model with Fuzzy C-Means
نویسنده
چکیده
This paper extends the traditional Fuzzy C-Means clustering method to a generalized fuzzy clustering model. According to most applications, this fuzzy clustering model briefly includes 3 parts: feature extractor transfers original objects information to desired feature data; fuzzy cluster analyzer gets cluster information from the feature data; and post treatment obtains the final results based on the cluster information. Among them, fuzzy cluster analyzer is encapsulated to 5 parts instead of traditional E-step and M-step: Initialization, α U , E-step, Distance Calculation, and M-step. This model makes each part keep relatively independent and easy to improve by just replacing one or several parts in needs. An implementation of this model is supplied, and 3 examples are given to test the properties. Moreover, potential optimizations are analyzed and listed.
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