NDLI: The fitting of binned data clustering to imprecise data

Content Provider	IEEE Xplore Digital Library
Author	Hamdan, H. Govaert, G.
Copyright Year	2004
Description	Author affiliation: Centre de Recherches de Royallieu, Univ. de Technologie de Compiegne, France (Hamdan, H.; Govaert, G.)
Abstract	This paper addresses the problem of taking into account the data imprecision in the clustering of binned data using mixture models and binned EM algorithm. Within the framework of a defects detection problem by acoustic emission control, we were brought to treat a set of points using the EM algorithm applied to a diagonal Gaussian mixture model. This one provides a satisfactory solution but the real time constraints imposed in our problem make its application impossible when the number of points becomes too big. As data sets become larger, data processing becomes increasingly complex and as a result, the data analysis is expensive in computation time. The solution that we propose is to group data and available data thus takes the form of a histogram. Such data are also called binned data. We fit the binning data procedure to imprecise data. We model imprecise data by multivariate uncertainty zones and we propose to assign each uncertainty zone to several bins with percentages proportional to its overlapping surfaces with the bins. The experimental results compare this binning procedure with the classical one (applied to imprecise points) and with the interval EM algorithm considered here as a reference, using simulated data.
Sponsorship	Syriatel Global Commun. Syrian Comput. Soc. Network HAMSHO Int. Phoenix Consulting DETECON: Detecon & Diebold Consultants Republique Francaise Ambassade de France en Syrie Syrian Airlines STE JUCI
Starting Page	557
Ending Page	558
File Size	124653
Page Count	2
File Format	PDF
ISBN	0780384822
DOI	10.1109/ICTTA.2004.1307883
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-04-23
Publisher Place	Syria
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Maximum likelihood estimation Histograms Machine learning algorithms Data analysis Uncertainty Clustering algorithms Machine learning Acoustic emission Data processing Acoustic signal detection
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
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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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