NDLI: Fuzzy c-Means Classifier for Relational Data

Content Provider	IEEE Xplore Digital Library
Author	Ichihashi, H. Honda, K. Kuramoto, Y. Matsuura, F.
Copyright Year	2007
Description	Author affiliation: Graduate Sch. of Eng., Osaka Prefecture Univ. (Ichihashi, H.; Honda, K.; Kuramoto, Y.; Matsuura, F.)
Abstract	This paper proposes a relational version of the fuzzy c-means (FCM) classifier in which relational data instead of object data are used. The classifier based on the relational clustering is called "relational classifier". The classifier is useful when a feature space has an extremely high dimensionality that exceeds the number of objects and many of the feature values are missing, or when only relational data are available instead of the object data. The relational data is represented by a matrix in terms of distances (dissimilarity) between object data, and is not concerned with the relational database. The clustering algorithm used in the classifier includes, as a special case, the relational dual of FCM proposed by Hathaway, Davenport and Bezdek and can be seen as a simultaneous application of multidimensional scaling and clustering. The computational intensity of the classifier is comparable to Gaussian mixture classifier (GMC). The proposed classifier outperforms well established relational classifier known as k-nearest neighbor (k-NN) on several benchmark datasets from the UCI ML repository
Starting Page	328
Ending Page	334
File Size	159332
Page Count	7
File Format	PDF
ISBN	1424407052
DOI	10.1109/CIDM.2007.368892
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-03-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy sets Clustering methods Shape measurement Clustering algorithms Relational databases Data engineering Entropy Covariance matrix Data mining Computational intelligence
Content Type	Text
Resource Type	Article

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