Clustering ellipses for anomaly detection

Pattern Recognition

Volumn 44, Issue 1, 2011, Pages 55-69

  Moshtaghi, Masud ^a,d   Havens, Timothy C ^b   Bezdek, James C ^a,b   Park, Laurence ^c   Leckie, Christopher ^a,d   Rajasegarar, Sutharshan ^a   Keller, James M ^b   Palaniswami, Marimuthu ^a  

^a UNIVERSITY OF MELBOURNE   (Australia)

^b University of Missouri   (United States)

^c UNIVERSITY OF WESTERN SYDNEY   (Australia)

^d CSIRO   (Australia)

Author keywords

Cluster analysis; Elliptical anomalies in wireless sensor networks; Reordered dissimilarity images; Similarity of ellipsoids; Single linkage clustering; Visual assessment

Indexed keywords

REORDERED DISSIMILARITY IMAGES; SIMILARITY OF ELLIPSOIDS; SINGLE LINKAGE CLUSTERING; VISUAL ASSESSMENTS; WIRELESS SENSOR;

CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; DATA VISUALIZATION; IMAGE PROCESSING; RECURSIVE FUNCTIONS; SENSOR NETWORKS;

WIRELESS SENSOR NETWORKS;

EID: 77956478694     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2010.07.024     Document Type: Article

References (40)

1. R.A. Johnson, and D.A. Wichern Applied Multivariate Statistical Analysis 3rd ed 1992 Prentice-Hall Englewood Cliffs, NJ
2. P.M. Kelly, D.R. Hush, and J.M. White An adaptive algorithm for modifying hyperellipsoidal decision surfaces J. Artif. Neural Networks 1 4 1994 459 480
3. P.M. Kelley An algorithm for merging hyperellipsoidal clusters (1994). TR LA-UR-94-3306 1994 Los Alamos National Laboratory Los Alamos, NM
4. R.N. Dav, K.J. Patel, Fuzzy ellipsoidal-shell clustering algorithm and detection of elliptical shapes, in: D.P. Casasent (Ed.), SPIE Proceedings of the Intelligent Robots and Computer Vision IX, vol. 1607, 1991, pp. 320333.
5. Y. Nakamori, and M. Ryoke Identification of fuzzy prediction models through hyperellipsoidal clustering IEEE Trans. Syst. Man Cybernet. 24 8 1994 1153 1173
6. J. Dickerson, and B. Kosko Fuzzy function learning with covariance ellipsoids in: Proceedings of the IEEE International Conference on Neural Networks 1993 IEEE Press Piscataway, NJ pp. 11621167
7. R. Duda, and P. Hart Pattern Classification and Scene Analysis 1973 Wiley Interscience New York
8. S. Rajasegarar, C. Leckie, M. Palaniswami, CESVM: centered hyperellipsoidal support vector machine based anomaly detection, in: Proceedings of the IEEE ICC 2008, 2008, pp. 16101614.
9. S. Rajasegarar, J.C. Bezdek, C. Leckie, M. Palaniswami, Analysis of anomalies in IBRL data from a wireless sensor network deployment, in: Proceedings of the International Conference on Sensor Technologies and Applications, 2007, pp. 158163.
10. S. Rajasegarar, J.C. Bezdek, C. Leckie, and M. Palaniswami Elliptical anomalies in wireless sensor networks ACM TOSN 6 1 2009 1550 1579
11. M. Moshtaghi, S. Rajasegarar, C. Leckie, S. Karunasekera, Anomaly detection by clustering ellipsoids in wireless sensor networks, in: Proceedings of the Fifth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2009), 710 December 2009, Melbourne, Australia, 2009.
12. J.C. Bezdek, T.C. Havens, J.M. Keller, C.A. Leckie, L Park, M. Palaniswami, S. Rajasegarar, Clustering elliptical anomalies in sensor networks, in Proceedings of the FUZZ-IEEE, Barcelona, 2010.
13. H.Y.T Ngan, G.K.H. Pang, and N.H.C. Yung Ellipsoidal decision regions for motif-based patterned fabric defect detection Pattern Recog. 43 2010 2132 2144
14. J.C. Bezdek, R.J. Hathaway, VAT: a tool for visual assessment of (cluster) tendency, in: Proceedings of the 2002 International Joint Conference on Neural Networks, Honolulu, HI, 2002, pp. 22252230.
15. T.C. Havens, J.C. Bezdek, A recursive formulation of the Improved Visual Assessment of Cluster Tendency (iVAT) Algorithm, IEEE TKDE, in review.
16. S. Theodoridis, and K. Koutroumbas Pattern Recognition 5th ed. 2010 Academic Press New York
17. J.C. Bezdek, J.M. Keller, R. Krishnapuram, and N.R. Pal Fuzzy Models and Algorithms for Pattern Recognition and Image Processing 1999 Kluwer Norwell
18. A. Jain, and R. Dubes Algorithms for Clustering Data 1988 Prentice Hall Englewood Cliffs, NJ
19. J. Hartigan Clustering Algorithms 1975 Wiley New York
20. R. Xu, and D.C. Wunsch Clustering 2009 IEEE Press Piscataway, NJ
21. I. Borg, and J. Lingoes Multidimensional Similarity Structure Analysis 1987 Springer-Verlag New York, NY
22. E.R. Tufte The Visual Display of Quantitative Information 2nd ed 2001 Graphics Press Cheshire, CT
23. I.H. Witten, and E. Frank Data Mining: Practical Machine Learning Tools and Techniques 2nd ed 2005 Morgan Kaufmann San Francisco, CA
24. T. Soukup, and I. Davidson Visual Data Mining: Techniques and Tools for Data Visualization and Mining 2002 Wiley New York, NY
25. B.S. Everitt, Graphical Techniques for Multivariate Data, North Holland, New York, 1978.
26. R.C. Tryon Cluster Analysis 1939 Edwards Bros. Ann Arbor, MI
27. R.B. Cattell A note on correlation clusters and cluster search methods Psychometrika 9 1944 169 184
28. P.H.A. Sneath A computer approach to numerical taxonomy J. Gen. Microbiol. 17 1957 201 226
29. G.D. Floodgate, and P.R. Hayes The Adansonian taxonomy of some yellow pigmented marine bacteria J. Gen. Microbiol. 30 1963 237 244
30. R.F. Ling A computer generated aid for cluster analysis Commun. ACM 16 1973 355 361
31. J.C. Bezdek, and R.J. Hathaway VAT: a tool for visual assessment of (cluster) tendency in: Proceedings of the IJCNN 2002 2002 IEEE Press Piscataway, NJ pp. 22252230
32. R.J. Hathaway, J.C. Bezdek, and J.M. Huband Scalable visual assessment of cluster tendency for large data sets Pattern Recogn. 39 2006 1315 1324
33. J.C. Bezdek, R.J. Hathaway, and J.M. Huband Visual assessment of clustering tendency for rectangular dissimilarity matrices IEEE Trans. Fuzzy Sys. 15 5 2007 890 903
34. L. Wang, T. Nguyen, J.C. Bezdek, C.A. Leckie, K. Ramamohanarao, iVAT and aVAT: enhanced visual analysis for cluster tendency assessment, in Proceedings of the PAKDD, Hyderabad, India, June 2010.
35. T.C. Havens, J.C. Bezdek, A recursive formulation of the Improved Visual Assessment of Cluster Tendency (iVAT) Algorithm, IEEE TKDE, in review.
36. N.R. Conn, N.I.M. Gould, Ph.L. Toint, Trust-Region Methods, MPS/SIAM Series on Optimization, SIAM and MPS, 2000.
37. T.C. Havens, J.C. Bezdek, J.M. Keller, and M. Popescu Clustering in ordered dissimilarity data Int. J. Intell. Sys. 24 5 2008 504 528
38. T.C. Havens, J.C. Bezdek, J.M. Keller, M. Popescu, and J.M. Huband Is VAT really single linkage in disguise? Ann. Math. Artif. Intell. 55 34 2009 237 251
39. Juhfisz Ferenc On the characteristic values of non-symmetric block random matrices J. Theoret. Probab. 3 2 1990 199 205
40. S. Fallah, D. Tritchler, and J. Beyene Estimating number of clusters based on a general similarity matrix with application to microarray data Statist. Appl. Genet. Mol. Biol. 7 1 2008 1 23