NDLI: On parameter setting in applying Dave's noise fuzzy clustering to Gaussian mixture models

Content Provider	IEEE Xplore Digital Library
Author	Ichihashi, H. Honda, K.
Copyright Year	2004
Description	Author affiliation: Dept. of Industrial Eng., Osaka Prefecture Univ., Japan (Ichihashi, H.; Honda, K.)
Abstract	Gaussian mixture models (GMM) for density estimation uses maximum likelihood approach, whereas fuzzy c-means (FCM) clustering is based on an objective function method. The close relationship between them has been pointed out. When applying the robust fuzzy clustering approach by Dave to the GMM, careful parameter setting is required. From the consideration of the Gustafson and Kessel's constraint we propose a way of defining a parameter in a fuzzy counterpart of the GMM. Numerical examples show that the Dave's noise clustering approach is quite robust for detecting linear clusters from heavily noisy data sets. This approach is further applied to a relational version in which clusters are formed using the matrix R of relational data corresponding to pairwise distances between objects.
Starting Page	1501
Ending Page	1506
File Size	498683
Page Count	6
File Format	PDF
ISBN	0780383532
ISSN	10987584
DOI	10.1109/FUZZY.2004.1375396
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-07-25
Publisher Place	Hungary
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gaussian noise Fuzzy sets Noise robustness Clustering algorithms Entropy Industrial engineering Information science Vectors Maximum likelihood detection Maximum likelihood estimation
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Theoretical Computer Science Software

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