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Clustering ellipses for anomaly detection

Version 2 2024-06-04, 06:12
Version 1 2016-11-29, 14:17
journal contribution
posted on 2024-06-04, 06:12 authored by M Moshtaghi, TC Havens, JC Bezdek, L Park, C Leckie, Sutharshan RajasegararSutharshan Rajasegarar, JM Keller, M Palaniswami
Comparing, 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 Recognition

Volume

44

Pagination

55-69

Location

Amsterdam, The Netherlands

ISSN

0031-3203

Language

eng

Publication classification

C Journal article, C1.1 Refereed article in a scholarly journal

Copyright notice

2010, Elsevier B.V.

Issue

1

Publisher

Elsevier B.V.