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Author Almeida, R.J. ♦ Kaymak, U. ♦ Sousa, J.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2008
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Clustering algorithms ♦ Accuracy ♦ Classification algorithms ♦ Partitioning algorithms ♦ Biological system modeling ♦ Databases ♦ Data models
Abstract This paper proposes extracting fuzzy rules from data using fuzzy possibilistic c-means and possibilistic fuzzy c-means algorithms, which provide more than one partition information: the typicality matrix and the membership matrix. Usually to extract fuzzy rules from data only one of the partition matrix is used, resulting in one rule per cluster. In our work we extract rules from both the membership partition matrix and the typicality matrix, resulting in deriving multiple rules for each cluster. These methods are applied to fuzzy modeling of four different classification problems: Iris, Wine, Wisconsin breast cancer and Altman data sets. The performance of the obtained models is compared and we consider the added value of the proposed approach in fuzzy modeling.
Description Author affiliation: Erasmus Sch. of Econ., Erasmus Univ. Rotterdam, Rotterdam (Almeida, R.J.; Kaymak, U.)
ISBN 9781424418183
ISSN 10987584
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2008-06-01
Publisher Place China
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 179.73 kB
Page Count 7
Starting Page 1964
Ending Page 1970

Source: IEEE Xplore Digital Library