NDLI: Bin-EM-CEM algorithms of spherical parsimonious Gaussian mixture models for binned data clustering

Content Provider	IEEE Xplore Digital Library
Author	Hamdan, H. Jingwen Wu
Copyright Year	2013
Description	Author affiliation: Dept. of Signal Process. & Electron. Syst., SUPELEC, Gif-sur-Yvette, France (Hamdan, H.; Jingwen Wu)
Abstract	EM algorithm is widely used in clustering domain because of its easy implementation and small storage space. CEM algorithm, which is considered as a classification version of EM algorithm, is another common used clustering algorithm. With the development of technology, we obtain more and more data. This results in slow computation of EM and CEM algorithms. Binning data seems to be efficient in gaining computation time by reducing the number of observations to the number of bins. Thus, EM and CEM algorithms applied to binned data were proposed: binned-EM and bin-EM-CEM algorithms. Moreover, fourteen parsimonious Gaussian mixture models, generated according to eigenvalue decomposition of the variance matrices of the mixture components, have less parameters than the most general model. By applying the EM and CEM algorithms of parsimonious models, estimation process is simplified and then accelerated. In this paper, to combine the advantages of binned data and parsimonious Gaussian mixture models, we develop bin-EM-CEM algorithms of spherical parsimonious Gaussian mixture models.
Starting Page	187
Ending Page	192
File Size	596241
Page Count	6
File Format	PDF
ISBN	9781479908288
e-ISBN	9781479908301
DOI	10.1109/INES.2013.6632808
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-19
Publisher Place	Costa Rica
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data models Accuracy Clustering algorithms Gaussian mixture model Signal processing algorithms Mathematical model
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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