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Title: Fuzzy clustering of spatial binary data (English)
Author: Dang, Mô
Author: Govaert, Gérard
Language: English
Journal: Kybernetika
ISSN: 0023-5954
Volume: 34
Issue: 4
Year: 1998
Pages: [393]-398
Summary lang: English
Category: math
Summary: An iterative fuzzy clustering method is proposed to partition a set of multivariate binary observation vectors located at neighboring geographic sites. The method described here applies in a binary setup a recently proposed algorithm, called Neighborhood EM, which seeks a partition that is both well clustered in the feature space and spatially regular [AmbroiseNEM1996]. This approach is derived from the EM algorithm applied to mixture models [Dempster1977], viewed as an alternate optimization method [Hathaway1986]. The criterion optimized by EM is penalized by a spatial smoothing term that favors classes having many neighbors. The resulting algorithm has a structure similar to EM, with an unchanged M-step and an iterative E-step. The criterion optimized by Neighborhood EM is closely related to a posterior distribution with a multilevel logistic Markov random field as prior [Besag1986,Geman1984]. The application of this approach to binary data relies on a mixture of multivariate Bernoulli distributions [Govaert1990]. Experiments on simulated spatial binary data yield encouraging results. (English)
Keyword: mixture models
MSC: 62H30
MSC: 62H86
MSC: 62M40
MSC: 65C60
idZBL: Zbl 1274.62418
Date available: 2009-09-24T19:17:45Z
Last updated: 2015-03-28
Stable URL:
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