A short survey on data clustering algorithms

Proceedings - 2015 2nd International Conference on Soft Computing and Machine Intelligence, ISCMI 2015

Volumn , Issue , 2016, Pages 64-68

  Wong, Ka Chun ^a  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

benchmark; cluster analysis; comparison; data clustering; data mining

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BENCHMARKING; BIOINFORMATICS; CLUSTER ANALYSIS; DATA MINING; SOFT COMPUTING; SPEECH RECOGNITION;

CLUSTERING EVALUATION; COMPARISON; DATA CLUSTERING; DATA CLUSTERING ALGORITHM; DESIGN CONCEPT; FINANCIAL ANALYSIS; SIMILARITY MEASURE; STATE OF THE ART;

CLUSTERING ALGORITHMS;

EID: 84964324621     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISCMI.2015.10     Document Type: Conference Paper

References (43)

1. K.-C. Wong and Z. Zhang, "Snpdryad: predicting deleterious nonsynonymous human snps using only orthologous protein sequences," Bioinformatics, p. btt769, Jan 2014.
2. K.-C. Wong, K.-S. Leung, and M.-H. Wong, "Effect of spatial locality on an evolutionary algorithm for multimodal optimization," in Proceedings of the 2010 international conference on Applications of Evolutionary Computation-Volume Part I, ser. EvoApplicatons'10. Berlin, Heidelberg: Springer-Verlag, 2010, pp. 481-490. [Online]. Available: http://dx.doi.org/10.1007/978-3-642-12239-2 50
3. K.-C. Wong, C. Peng, M.-H. Wong, and K.-S. Leung, "Generalizing and learning protein-dna binding sequence representations by an evolutionary algorithm," Soft Comput., vol. 15, no. 8, pp. 1631-1642, Aug. 2011. [Online]. Available: http://dx.doi.org/10.1007/s00500-011-0692-5
4. K.-C. Wong, C. Peng, and Y. Li, "Probabilistic inference on multiple normalized signal profiles from next generation sequencing: Transcription factor binding sites," Computational Biology and Bioinformatics, IEEE/ACM Transactions on, vol. PP, no. 99, pp. 1-1, 2015.
5. T. Gonzalez, "On the computational complexity of clustering and related problems," in System Modeling and Optimization, ser. Lecture Notes in Control and Information Sciences, R. Drenick and F. Kozin, Eds. Springer Berlin/Heidelberg, 1982, vol. 38, pp. 174-182, 10.1007/BFb0006133. [Online]. Available: http://dx.doi.org/10.1007/BFb0006133
6. H. Steinhaus, "Sur la division des corps ma4eriels en parties." Bull. Acad. Pol. Sci., Cl. III, vol. 4, pp. 801-804, 1957.
7. H. Xiong, J. Wu, and J. Chen, "K-means clustering versus validation measures: A data-distribution perspective," IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 39, no. 2, pp. 318-331, 2009.
8. D. Arthur and S. Vassilvitskii, "k-means++: the advantages of careful seeding," in Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, ser. SODA '07. Philadelphia, PA, USA: Society for Industrial and Applied Mathematics, 2007, pp. 1027-1035. [Online]. Available: http://portal.acm.org/citation.cfm?id=1283383.1283494
9. R. Xu and D. Wunsch, "Survey of Clustering Algorithms," IEEE Transactions on Neural Networks, vol. 16, no. 3, pp. 645-678, May 2005. [Online]. Available: http://dx.doi.org/10.1109/TNN.2005.845141
10. G. Karypis, E.-H. S. Han, and V. Kumar, "Chameleon: Hierarchical clustering using dynamic modeling," Computer, vol. 32, pp. 68-75, August 1999. [Online]. Available: http://dx.doi.org/10.1109/2.781637
11. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise," in Proc. of 2nd International Conference on Knowledge Discovery and Data Mining, 1996, pp. 226-231.
12. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, "Automatic subspace clustering of high dimensional data for data mining applications," SIGMOD Rec., vol. 27, pp. 94-105, June 1998. [Online]. Available: http://doi.acm.org/10.1145/276305.276314
13. N. Bansal, A. Blum, and S. Chawla, "Correlation clustering," Machine Learning Journal, vol. Special Issue on Theoretical Advances in Data Clustering, pp. 86-113, 2004.
14. A. Y. Ng, M. I. Jordan, and Y. Weiss, "On spectral clustering: Analysis and an algorithm," in Advances in Neural Information Processing Systems. MIT Press, 2001, pp. 849-856.
15. J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, pp. 888-905, August 2000. [Online]. Available: http://dx.doi.org/10.1109/34.868688
16. M. Maila and J. Shi, "A random walks view of spectral segmentation," in AI and STATISTICS (AISTATS) 2001, 2001.
17. W. Wright, "Gravitational clustering," Pattern Recognition, vol. 9, no. 3, pp. 151-166, 1977. [Online]. Available: http://www. sciencedirect.com/science/article/pii/0031320377900139
18. J. Gomez, D. Dasgupta, and O. Nasraoui, "A new gravitational clustering algorithm," in In Proc. of the SIAM Int. Conf. on Data Mining (SDM, 2003.
19. T. Long and L.-W. Jin, "A new simplified gravitational clustering method for multi-prototype learning based on minimum classification error training," in Advances in Machine Vision, Image Processing, and Pattern Analysis, ser. Lecture Notes in Computer Science, N. Zheng, X. Jiang, and X. Lan, Eds. Springer Berlin/Heidelberg, 2006, vol. 4153, pp. 168-175.
20. X. Wang, W. Qiu, and R. H. Zamar, "Clues: A non-parametric clustering method based on local shrinking," Computational Statistics & Data Analysis, vol. 52, no. 1, pp. 286-298, September 2007. [Online]. Available: http://ideas.repec.org/a/eee/csdana/v52y2007i1p286-298.html
21. K. Blekas and I. E. Lagaris, "Newtonian clustering: An approach based on molecular dynamics and global optimization," Pattern Recogn., vol. 40, pp. 1734-1744, June 2007. [Online]. Available: http://portal.acm.org/citation.cfm?id=1231536.1231754
22. K.-C. Wong, C. Peng, Y. Li, and T.-M. Chan, "Herd clustering: A synergistic data clustering approach using collective intelligence," Applied Soft Computing, vol. 23, pp. 61-75, 2014.
23. C. Peng, X. Jin, K.-C. Wong, M. Shi, and P. Lío, "Collective human mobility pattern from taxi trips in urban area," PloS one, vol. 7, no. 4, p. e34487, 2012.
24. U. Maulik and S. Bandyopadhyay, "Genetic algorithm-based clustering technique," Pattern Recognition, vol. 33, no. 9, pp. 1455-1465, 2000. [Online]. Available: http://www.sciencedirect.com/science/article/B6V14-40961WK-5/2/41f336383004b7f397ae8e9266f90b0a
25. A. Likas, N. Vlassis, and J. J. Verbeek, "The global k-means clustering algorithm," Pattern Recognition, vol. 36, no. 2, pp. 451-461, 2003. [Online]. Available: http://www.sciencedirect.com/science/article/B6V14-45TTJY0-7/2/1bfdf4def0cf8b6ba77fd25132df8d0d
26. G. Celeux and G. Govaert, "Gaussian parsimonious clustering models," Pattern Recognition, vol. 28, no. 5, pp. 781-793, 1995. [Online]. Available: http://www.sciencedirect.com/science/article/B6V14-3YGV1N5-16/2/2f50595a70e52e39054440b62548439e
27. H. Frigui and R. Krishnapuram, "Clustering by competitive agglomeration," Pattern Recognition, vol. 30, no. 7, pp. 1109-1119, 1997. [Online]. Available: http://www.sciencedirect.com/science/article/B6V14-3SNVHWM-M/2/3c5ec045f0a0662d00ab583aab028f9f
28. K. C. Gowda and E. Diday, "Symbolic clustering using a new dissimilarity measure," Pattern Recognition, vol. 24, no. 6, pp. 567-578, 1991. [Online]. Available: http://www.sciencedirect.com/science/article/B6V14-48MPNTW-1JS/2/7ae7f4c958c1acf937f4f02091c5d073
29. E. C.-K. Tsao, J. C. Bezdek, and N. R. Pal, "Fuzzy kohonen clustering networks," Pattern Recognition, vol. 27, no. 5, pp. 757-764, 1994. [Online]. Available: http://www.sciencedirect.com/science/article/B6V14-48MPPXD-1YX/2/cf0e26b7498b3a4f4b7698975495f7f9
30. L. Zhu, F.-L. Chung, and S. Wang, "Generalized fuzzy cmeans clustering algorithm with improved fuzzy partitions," IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 39, pp. 578-591, June 2009. [Online]. Available: http://dl.acm.org/citation. cfm?id=1656753.1656754
31. J.-S. Zhang and Y.-W. Leung, "Robust clustering by pruning outliers." IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 33, no. 6, pp. 983-998, 2003. [Online]. Available: http://dblp.uni-trier.de/db/journals/tsmc/tsmcb33.html#ZhangL03
32. C. F. M. F. R. S. Filippone, M., "A survey of kernel and spectral methods for clustering," Pattern Recognition, vol. 41, no. 1, pp. 176-190, 2008. [Online]. Available: http://www.scopus.com/inward/record.url?eid=2-s2.0-34548025132& partnerID=40&md5=2d78a0f28c666bd207ce98688b66ca9b
33. P. Maji, "Fuzzy-rough supervised attribute clustering algorithm and classification of microarray data," IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 41, no. 1, pp. 222-233, 2011.
34. W. Hu, W. Hu, N. Xie, and S. Maybank, "Unsupervised active learning based on hierarchical graph-theoretic clustering," IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 39, pp. 1147-1161, October 2009. [Online]. Available: http://dl.acm.org/citation.cfm?id=1656796.1656802
35. P. Corsini, B. Lazzerini, and F. Marcelloni, "A fuzzy relational clustering algorithm based on a dissimilarity measure extracted from data," IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 34, no. 1, pp. 775-781, feb. 2004.
36. A. Baraldi and P. Blonda, "A survey of fuzzy clustering algorithms for pattern recognition. i," IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 29, no. 6, pp. 778-785, 1999.
37. K.-C. Wong, C.-H. Wu, R. K. P. Mok, C. Peng, and Z. Zhang, "Evolutionary multimodal optimization using the principle of locality," Inf. Sci., vol. 194, pp. 138-170, Jul. 2012. [Online]. Available: http://dx.doi.org/10.1016/j.ins.2011.12.016
38. A. Frank and A. Asuncion, "UCI machine learning repository," 2010. [Online]. Available: http://archive.ics.uci.edu/ml
39. M. Halkidi, Y. Batistakis, and M. Vazirgiannis, "On clustering validation techniques," J. Intell. Inf. Syst., vol. 17, pp. 107-145, December 2001. [Online]. Available: http://portal.acm.org/citation.cfm? id=607585.607609
40. Y. Zhao and G. Karypis, "Criterion functions for document clustering: Experiments and analysis," University of Minnesota, Department of Computer Science, Tech. Rep., 2002.
41. N. X. Vinh, J. Epps, and J. Bailey, "Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance," J. Mach. Learn. Res., vol. 11, pp. 2837-2854, Dec. 2010. [Online]. Available: http://dl.acm.org/citation.cfm? id=1756006.1953024
42. K.-C. Wong, Y. Li, C. Peng, and Z. Zhang, "Signalspider: probabilistic pattern discovery on multiple normalized chip-seq signal profiles," Bioinformatics, p. btu604, 2014.
43. K.-C. Wong, Y. Li, C. Peng, and H.-S. Wong, "A comparison study for dna motif modeling on protein binding microarray," Computational Biology and Bioinformatics, IEEE/ACM Transactions on, vol. PP, no. 99, pp. 1-1, 2015.