Fuzzy Clustering, Feature Selection, and Membership Function Optimization
نویسنده
چکیده
This paper explores the topic of fuzzy clustering, feature selection, and membership function optimization. Feature selection plays a crucial role for all fuzzy clustering applications, as the selection of appropriate features determines the quality of the resulting clusters. We will show how fuzzy clustering can be applied to data mining problems by introducing some of the most commonly used clustering algorithms. For fuzzy clustering, membership values are assigned to each data point. When the same clustering criteria have to be applied to new data, it can be helpful to have the notion of a membership function which allows to determine the membership values of each data point without repeating the clustering for the new data set. We will therefore present methods to perform membership function optimization based on statistical, genetic and neuro-fuzzy function approximation algorithms in order to provide a short survey of the stateof-the-art.
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