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Measures for clustering and anomaly detection in sets of higher dimensional ellipsoids
conference contribution
posted on 2012-08-22, 00:00 authored by Sutharshan RajasegararSutharshan Rajasegarar, J C Bezdek, M Moshtaghi, C Leckie, T C Havens, M PalaniswamiOne 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.
History
Event
IEEE World Congress on Computational Intelligence (2012 : Brisbane, Queensland)Pagination
1 - 8Publisher
IEEELocation
Brisbane, QueenslandPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2012-06-10End date
2012-06-15ISBN-13
9781467314909Language
engPublication classification
E Conference publication; E1.1 Full written paper - refereedCopyright notice
2012, US Government work not protected by US copyrightTitle of proceedings
WCCI 2012 : Proceedings of the IEEE World Congress on Computational IntelligenceUsage metrics
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