Adaptive learning for event modeling and characterization

Pattern Recognition

Volumn 40, Issue 5, 2007, Pages 1544-1555

  Dai, Shuangshuang ^a   Dhawan, Atam P ^a  

^a NEW JERSEY INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Adaptive learning; Event modeling; Fuzzy k means clustering; Hierarchical clustering

Indexed keywords

ADAPTIVE SYSTEMS; COMPUTER SIMULATION; DATA ACQUISITION; FUZZY SETS; MATHEMATICAL MODELS; PATTERN RECOGNITION; WAVELET TRANSFORMS;

ADAPTIVE LEARNING; EVENT MODELING; FUZZY K-MEANS CLUSTERING; HIERARCHICAL CLUSTERING;

LEARNING SYSTEMS;

EID: 33846267244     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2006.09.018     Document Type: Article

References (39)

1. Jain A.K., and Dubes R.C. Algorithms for Clustering Data (1998), Prentice-Hall, Englewood Cliffs, NJ
2. Y. Yang, T. Pierce, J. Carbonell, A study on retrospective and on-line event detection, in: Proceedings of the 21th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR98), Melbourne, Australia, August 1998, pp. 28-36.
3. Zadeh L.A. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1 (1978) 3-28
4. Fisher D. Iterative optimization and simplification of hierarchical clustering. J. Artif. Intell. Res. 4 (1996) 147-179
5. G. Karypis, E.-H. Han, Multilevel refinement for hierarchical clustering, Technical Report #99-020, 1999.
6. Dempster A.P., Laird N.M., and Rubin D.B. Maximum likelihood for incomplete data via the EM algorithm. J. Roy. Stat. Soc. 39 (1977) 1-38
7. Mukerjee S., Feigelson E.D., Babu G.J., Murtagh F., Fraley C., and Raftery A.E. Three types of gamma ray bursts. Astrophys. J. 508 (1998) 314-327
8. G. Doddington, Presentation Slides at The DARPA Broadcast News Workshop, VA, 1999.
9. Lombard L.B. Events: A Metephysical Study (1986), Routledge & Kegan Paul, Boston, MA
10. Dasgupta A., and Raftery A.E. Detecting features in spatial point processes with clutter via model-based clustering. J. Am. Stat. Assoc. 93 (1998) 294-302
11. R. Papka, On-line new event detection, clustering, and tracking, Ph.D. Dissertation, University of Massachusetts, Amherst, MA, 1999.
12. Murtagh F. A survey of recent advances in hierarchical clustering algorithms. Comput. J. 26 4 (1983) 354-359
13. Olson C. Parallel algorithms for hierarchical clustering. Parallel Comput. 21 (1995) 1313-1325
14. McQueen J. Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (1967) 281-297
15. Diday E. The dynamic cluster method in non-hierarchical clustering. J. Comput. Inf. Sci. 2 (1973) 61-88
16. Symon M.J. Clustering criterion and multivariate normal mixture. Biometrics 77 (1977) 35-43
17. E.H. Ruspini, A new approach to clustering, Inf. Control 15 (1969) 22-32.
18. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms (1981), Plenum Press, New York, NY
19. Dave R.N. Generalized fuzzy C-shells clustering and detection of circular and elliptic boundaries. Pattern Recognition 25 (1992) 713-722
20. Daubechies I. The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. Inf. Theory 36 (1990) 961-1005
21. Daubechies I. Orthonormal bases of compactly supported wavelets. Commun. Pure Appl. Math. 41 (1988) 909-996
22. Heil C.E., and Walnut D.F. Continuous and discrete wavelet transforms. SIAM Rev. 31 (1989) 628-666
23. Mallat S.G. Multiresolution approximation and wavelet orthonormal bases of L2. Trans. Amer. Math. Soc. 315 1 (1989) 69-87
24. Vidakovic B. Statistical Modeling by Wavelets (1999), Wiley, New York
25. Morettin P.A. From Fourier to wavelet analysis of time series. Proceedings in Computational Statistics (1996) 111-122
26. Prasad L., Iyengar S.S., and Ayengar S.S. Wavelet Analysis with Applications to Image Processing (1997), CRC Press, Boca Raton, FL
27. Coifman R.R., and Wickerhauser M.V. Entropy-based algorithms for best basis selection. IEEE Trans. Inf. Theory 38 (1992) 713-718
28. Li T., Li Q., Zhu S., and Qgihara M. A survey on wavelet application in data mining. SIGKDD Explorations 4 2 (2001) 49-68
29. P. Bradley, U. Fayyad, Refining initial points for k-means clustering, in: Proceedings of the 15th ICML, Madison, WI, 1998, pp. 91-99.
30. B. Zhang, Generalized k-harmonic means-dynamic weighting of data in unsupervised learning, in: Proceedings of the First SIAMICDM, Chicago, IL, 2001.
31. Strang G. Wavelets and dilation equations: a brief introduction. SIAM Rev. 31 (1989) 614-627
32. Mallat S.G. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11 (1989) 674-693
33. Mannila T., Smyth H., and Hand D.J. Principles of Data Mining (2001), MIT Press, New York
34. Bishop C.M. Neural Networks for Pattern Recognition (1994), Oxford Press, Oxford
35. S. Dai, Adaptive learning for event modeling and pattern classification, Ph.D. Dissertation, New Jersey Institute of Technology, January 2004.
36. S. Dai, A.P. Dhawan, PCA-based fuzzy classification of turbine blade fatigue modes, in: Proceedings of 40th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, 2004, pp. 4046-4054.
37. J.C. Bezdek, Fuzzy mathematics in pattern classification, Ph.D. Dissertation, Cornell University, Ithaca, NY, 1973.
38. Duda R.O., Hart P.E., and Stork D.G. Pattern Classification. second ed. (2001), Wiley, New York
39. Pham D.L., and Prince J.L. Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans. Med. Imaging 18 9 (1999) 737-752