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Unsupervised Topographic Learning for Spatiotemporal Data Mining
Joint Authors
Cabanes, Guénaël
Bennani, Younès
Source
Advances in Artificial Intelligence
Issue
Vol. 2010, Issue 2010 (31 Dec. 2010), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2010-11-28
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Science
Abstract EN
In recent years, the size and complexity of datasets have shown an exponential growth.
In many application areas, huge amounts of data are generated, explicitly or implicitly containing spatial or spatiotemporal information.
However, the ability to analyze these data remains inadequate, and the need for adapted data mining tools becomes a major challenge.
In this paper, we propose a new unsupervised algorithm, suitable for the analysis of noisy spatiotemporal Radio Frequency IDentification (RFID) data.
Two real applications show that this algorithm is an efficient data-mining tool for behavioral studies based on RFID technology.
It allows discovering and comparing stable patterns in an RFID signal and is suitable for continuous learning.
American Psychological Association (APA)
Cabanes, Guénaël& Bennani, Younès. 2010. Unsupervised Topographic Learning for Spatiotemporal Data Mining. Advances in Artificial Intelligence،Vol. 2010, no. 2010, pp.1-12.
https://search.emarefa.net/detail/BIM-501759
Modern Language Association (MLA)
Cabanes, Guénaël& Bennani, Younès. Unsupervised Topographic Learning for Spatiotemporal Data Mining. Advances in Artificial Intelligence No. 2010 (2010), pp.1-12.
https://search.emarefa.net/detail/BIM-501759
American Medical Association (AMA)
Cabanes, Guénaël& Bennani, Younès. Unsupervised Topographic Learning for Spatiotemporal Data Mining. Advances in Artificial Intelligence. 2010. Vol. 2010, no. 2010, pp.1-12.
https://search.emarefa.net/detail/BIM-501759
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-501759