APSCAN: A parameter free algorithm for clustering

Pattern Recognition Letters

Volumn 32, Issue 7, 2011, Pages 973-986

  Chen, Xiaoming ^a,b   Liu, Wanquan ^b   Qiu, Huining ^a,b   Lai, Jianhuang ^a  

^a SUN YAT SEN UNIVERSITY   (China)

^b CURTIN UNIVERSITY   (Australia)

Author keywords

Affinity propagation algorithm; Clustering algorithm; DBSCAN

Indexed keywords

A-DENSITY; AFFINITY PROPAGATION; CLUSTERING RESULTS; DATA POINTS; DATA SETS; DBSCAN; DENSITY PARAMETERS; DENSITY-BASED; INPUT PARAMETER; LOCAL DENSITY; NONLINEAR DATA; PRIOR KNOWLEDGE; SPATIAL DATASETS; TWO PARAMETER;

DATA STRUCTURES; PARAMETER ESTIMATION;

CLUSTERING ALGORITHMS;

EID: 79952106003     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2011.02.001     Document Type: Article

References (33)

1. Agrawal, R.; Gehrke, J.; Gunopulos, D.; Raghavan, P.; 1998. Automatic subspace clustering of high dimensional data for data mining applications. In: Proc. ACM SIGMOD Internat. Conf. on the Management of Data, pp. 94-105. (Pubitemid 128655960)
2. H.A. Ben, D. Horn, H.T. Siegelmann, and V. Vapnik Support vector clustering J. Machine Learn. Res. 2 2002 125 137
3. D. Birant, and A. Kut ST-DBSCAN: An algorithm for clustering spatial-temporal data Data Knowl. Eng. 60 2007 208 221 (Pubitemid 46053593)
4. Castro, V.E.; Yang, J.; 2000. A fast and robust general purpose clustering algorithm. In: Proc. 6th Pacific Rim Internat. Conf. on Artificial Inetlligence, pp.208-218.
5. Cristianini, N.; Taylor, J.S.; Kandola, J.S.; 2001. Spectral kernel methods for clustering. In: NIPS 14, pp. 649-655.
6. Dash, M.; Liu, H.; Xu, X.; 2001. '1 + 1 > 2': Merging distance and density based clustering. In: Proc. 7th Internat. Conf. on Database Systems for Advanced Application, pp. 32-39.
7. Dueck, D.; Frey, B.J.; 2007. Non-metric affinity propagation for unsupervised image categortization. In: Proc. 11th Internat. Conf. on Computer Vision, pp.1-8.
8. Ester, M.; Kriegel, H.P.; Sander, J.; Xu, X.W.; 1996. A density-based alogrithm for discovering clusters. In: Proc. Internat. Conf. on Very Large Databased, pp. 28-39.
9. B.S. Everitt, S. Landau, and M. Leese Cluster analysis fourth ed. 2001 Arnold London
10. M. Filippone, F. Camastra, F. Masulli, and S. Rovetta A survey of kernel and spectral methods for clustering Pattern Recognition 41 1 2008 176 190 (Pubitemid 47284212)
11. E. Forgy Cluster analysis of multivariate data: Efficiency vs. interpretability of classifications Biometrics 21 1965 768 780
12. Frey, B.J.; Dueck, D.; 2005. Mixture modeling by affinity propagation. In: NIPS 18, pp. 379-386.
13. B.J. Frey, and D. Dueck Clustering by passing message between data points Science 315 2007 972 976 (Pubitemid 46281181)
14. M. Girolami Mercer kernel based clustering in feature space IEEE Trans. Neural Networks 13 3 2002 780 784 (Pubitemid 34669666)
15. R.H. Güting An introduction to spatial database systems J. VLDB 3 4 1994 357 399
16. Hinneburg, A. Keim, D.A.; 1998. An efficient approach to clustering in large multimedia databases with noise. In: Proc. 4th Internat. Conf. on Knowledge Discovery and Data Mining, pp. 58-65.
17. H. Iwabuchi, and T. Hayasaka Effects of cloud horiziontal inhomogeneity on the optical thickness retrieved from moderate-resolution satellites data J. Atmos. Sci. 59 14 2002 2227 2242
18. A.K. Jain Data clustering: 50 years beyond K-means Patter Recognition Lett. 31 8 2010 651 666
19. A.K. Jain, and R.C. Dubes Algorithms for Clustering Data 1988 Prentice-Hall Englewood Cliffs, NJ
20. S.C. Johnson Hierarchical clustering scheme Psychometrika 32 3 1967 241 254
21. L. Kaufman, and P.J. Rousseeuw Finding Groups in Data: An Introduction to Cluster Analysis Wiley Series in Probability and Mathematical Statistics 1990 John Wiley & Sons, Inc New York
22. T. Kohonen Median string Pattern Recognition Lett. 3 5 1985 309 313
23. A. Likas, N. Vlassis, and J.J. Verbeek The global K-means clustering algorithm Pattern Recognition 36 2 2003 451 461 (Pubitemid 35212840)
24. Y.N. Liu, and F. Wu Multi-modality video shot clustering with tensor representation J. Multimed. Tools Appl. 41 2008 93 109
25. MacQueen, J.; 1967. Some methods for classification and analysis of multivariate observations. In: Proc: 5th Berkeley Symp.; pp. 281-297.
26. J. Sander, M. Ester, H.P. Kriegel, and X.W. Xu Density-based clustering in spatial databases: The alogrithm GDBSCAN and its applications Data Min. Knowl. Disc. 2 1998 169 194
27. B. Schölkopf, A. Smola, and K.R. Müller Nonlinear component analysis as a kernel eigenvalue problem Neural Comput. 10 5 1998 1299 1319
28. H. Spath Clustering Analysis Algorithms 1980 Ellis Horwood
29. P. Viswanath, and V.S. Babu Rough-DBSCAN: A fast hybrid density based clustering method for large data sets Pattern Recognition Lett. 30 16 2009 1477 1488
30. Viswanath, P.; Pinkesh, R.; 2006. l-DBSCAN: A fast hybrid density based clustering method. In: Proc. 18th Internat. Conf. on Pattern Recognition, pp. 912-915. (Pubitemid 46532048)
31. R. Xu, and D. Wunsch II Survey of clustering algorithms IEEE Trans. Neural Network 16 3 2005 645 678 (Pubitemid 40718010)
32. Ye, Q.X.; Wen, Gao.; Zhang, W.; 2003. Color image segmentation using density-based clustering. In: Proc. 28th IEEE Internat. Conf. on Acoustics, Speech, and Signal Processing, pp. 345-348.
33. Zhang, X.; Gao, J.; Lu, P.; Yan, Y.H.; 2008. A novel speaker clustering algorithm via supervised affinity propagation. In: Proc. 33rd IEEE Internat. Conf. on Acoustics, Speech, and Signal Processing, pp. 245-248.