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Measures for clustering and anomaly detection in sets of higher dimensional ellipsoids

Rajasegarar, Sutharshan, Bezdek, James C., Moshtaghi, Masud, Leckie, Christopher, Havens, Timothy C. and Palaniswami, Marimuthu 2012, Measures for clustering and anomaly detection in sets of higher dimensional ellipsoids, in WCCI 2012 : Proceedings of the IEEE World Congress on Computational Intelligence, IEEE, Piscataway, N.J., pp. 1-8, doi: 10.1109/IJCNN.2012.6252703.

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Title Measures for clustering and anomaly detection in sets of higher dimensional ellipsoids
Author(s) Rajasegarar, SutharshanORCID iD for Rajasegarar, Sutharshan orcid.org/0000-0002-6559-6736
Bezdek, James C.
Moshtaghi, Masud
Leckie, Christopher
Havens, Timothy C.
Palaniswami, Marimuthu
Conference name IEEE World Congress on Computational Intelligence (2012 : Brisbane, Queensland)
Conference location Brisbane, Queensland
Conference dates 2012/06/10 - 2012/06/15
Title of proceedings WCCI 2012 : Proceedings of the IEEE World Congress on Computational Intelligence
Publication date 2012
Conference series IEEE World Congress on Computational Intelligence
Start page 1
End page 8
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
NETWORKS
Summary One of the applications that motivates this research is a system for detection of the anomalies in wireless sensor networks (WSNs). Individual sensor measurements are converted to ellipsoidal summaries; a data matrix D is built using a dissimilarity measure between pairs of ellipsoids; clusters of ellipsoids are suggested by dark blocks along the diagonal of an iVAT (improved Visual Assessment of Tendency) image of D; and finally, the single linkage algorithm extracts clusters from D, using the iVAT image as a guide to the selection of an optimal partition. We illustrate this model for higher dimensional data with synthetic, real and benchmark data sets. Our examples show that two of the four measures, viz, Focal distance and Bhattacharyya distance, provide very similar and reliable bases for estimating cluster structures in sets of higher dimensional ellipsoids, that single linkage can successfully extract the indicated clusters, and that our model can find both first and second order anomalies in WSN data.
ISBN 9781467314909
Language eng
DOI 10.1109/IJCNN.2012.6252703
Field of Research 080608 Information Systems Development Methodologies
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2012, US Government work not protected by US copyright
Free to Read? Yes
Use Rights Public Domain Mark
Persistent URL http://hdl.handle.net/10536/DRO/DU:30089655

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.