Iterative fuzzy clustering algorithm with supervision to construct probabilistic fuzzy rule base from numerical data

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To deal with data patterns with linguistic ambiguity and with probabilistic uncertainty in a single framework, we construct an interpretable probabilistic fuzzy rule-based system that requires less human intervention and less prior knowledge than other state of the art methods. Specifically, we present a new iterative fuzzy clustering algorithm that incorporates a supervisory scheme into an unsupervised fuzzy clustering process. The learning process starts in a fully unsupervised manner using fuzzy c-means (FCM) clustering algorithm and a cluster validity criterion, and then gradually constructs meaningful fuzzy partitions over the input space. The corresponding fuzzy rules with probabilities are obtained through an iterative learning process of selecting clusters with supervisory guidance based on the notions of cluster-pureness and class-separability. The proposed algorithm is tested first with synthetic data sets and benchmark data sets from the UCI Repository of Machine Learning Database and then, with real facial expression data and TV viewing data.
Publisher
IEEE-Inst Electrical Electronics Engineers Inc
Issue Date
2008-02
Language
English
Article Type
Article
Keywords

CLASSIFICATION; VALIDITY; CLASSIFIERS; MODEL

Citation

IEEE TRANSACTIONS ON FUZZY SYSTEMS, v.16, no.1, pp.263 - 277

ISSN
1063-6706
DOI
10.1109/TFUZZ.2007.903314
URI
http://hdl.handle.net/10203/93191
Appears in Collection
EE-Journal Papers(저널논문)
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