NDLI: Mixture Model Clustering of Uncertain Data

Content Provider	IEEE Xplore Digital Library
Author	Hamdan, H. Govaert, G.
Copyright Year	2005
Description	Author affiliation: CETIM, Senlis (Hamdan, H.)
Abstract	This paper addresses the problem of fitting mixture densities to uncertain data using the EM algorithm. Uncertain data are modelled by multivariate uncertainty zones which constitute a generalization of multivariate interval-valued data. We develop an EM algorithm to treat uncertainty zones around points of $Ropf^{p}$ in order to estimate the parameters of a mixture model defined on $Ropf^{p}$ and obtain a fuzzy clustering or partition. This EM algorithm requires the evaluation of multidimensional integrals over each uncertainty zone at each iteration. In the diagonal Gaussian mixture model case, these integrals can be computed by simply using the one-dimensional normal cumulative distribution function. Results on simulated data indicate that the proposed algorithm can estimate the true underlying density better than the classical EM algorithm applied to the imprecise data, especially when the imprecision degree is high
Sponsorship	IEEE IEEE Neural Networks Soc
Starting Page	879
Ending Page	884
File Size	2084293
Page Count	6
File Format	PDF
ISBN	0780391594
DOI	10.1109/FUZZY.2005.1452510
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-05-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Partitioning algorithms Uncertainty Iterative algorithms Parameter estimation Heuristic algorithms Prototypes Multidimensional systems Distributed computing Distribution functions
Content Type	Text
Resource Type	Article

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