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Clustering ellipses for anomaly detection
journal contribution
posted on 2011-01-01, 00:00 authored by M Moshtaghi, T C Havens, J C Bezdek, L Park, C Leckie, Sutharshan RajasegararSutharshan Rajasegarar, J M Keller, M PalaniswamiComparing, clustering and merging ellipsoids are problems that arise in various applications, e.g., anomaly detection in wireless sensor networks and motif-based patterned fabrics. We develop a theory underlying three measures of similarity that can be used to find groups of similar ellipsoids in p-space. Clusters of ellipsoids are suggested by dark blocks along the diagonal of a reordered dissimilarity image (RDI). The RDI is built with the recursive iVAT algorithm using any of the three (dis) similarity measures as input and performs two functions: (i) it is used to visually assess and estimate the number of possible clusters in the data; and (ii) it offers a means for comparing the three similarity measures. Finally, we apply the single linkage and CLODD clustering algorithms to three two-dimensional data sets using each of the three dissimilarity matrices as input. Two data sets are synthetic, and the third is a set of real WSN data that has one known second order node anomaly. We conclude that focal distance is the best measure of elliptical similarity, iVAT images are a reliable basis for estimating cluster structures in sets of ellipsoids, and single linkage can successfully extract the indicated clusters.
History
Journal
Pattern RecognitionVolume
44Issue
1Pagination
55 - 69Publisher
Elsevier B.V.Location
Amsterdam, The NetherlandsPublisher DOI
ISSN
0031-3203Language
engPublication classification
C Journal article; C1.1 Refereed article in a scholarly journalCopyright notice
2010, Elsevier B.V.Usage metrics
Categories
Keywords
cluster analysiselliptical anomalies in wireless sensor networksreordered dissimilarity imagessimilarity of ellipsoidssingle linkage clusteringvisual assessmentScience & TechnologyTechnologyComputer Science, Artificial IntelligenceEngineering, Electrical & ElectronicComputer ScienceEngineeringTENDENCYInformation SystemsArtificial Intelligence and Image Processing
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