A distance-relatedness dynamic model for clustering high dimensional data of arbitrary shapes and densities

Pattern Recognition

Volumn 42, Issue 7, 2009, Pages 1193-1209

  Yousri, Noha A ^a,b   Kamel, Mohamed S ^b   Ismail, Mohamed A ^a  

^a Alexandria University The Faculty Of Engineering   (Egypt)

^b UNIVERSITY OF WATERLOO   (Canada)

Author keywords

Arbitrary density clusters; Arbitrary shaped clusters; Clustering; Distance relatedness; Dynamic model; High dimensional data

Indexed keywords

COMPUTATIONAL COMPLEXITY; DATA VISUALIZATION; DYNAMIC MODELS;

ARBITRARY DENSITY CLUSTERS; ARBITRARY SHAPED CLUSTERS; CLUSTERING; DISTANCE-RELATEDNESS; HIGH DIMENSIONAL DATA;

CLUSTERING ALGORITHMS;

EID: 62349090984     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2008.08.037     Document Type: Article

References (36)

1. Shamir R., and Sharan R. Algorithmic approaches to clustering gene expression data. Current Topics in Computational Biology (2002), MIT Press, Cambridge, MA 269-299
2. Lim Y.W., and Lee S.U. On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques. Pattern Recognition 23 9 (1990) 935 952
3. T. Oates, M.D. Schmill, P.R. Cohen, A method for clustering the experiences of a mobile robot that accords with human judgments, Copyright © 2000, American Association for Artificial Intelligence.
4. L. Ertöz, M. Steinbach, V. Kumar, Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data, in: Proceedings of Second SIAM International Conference on Data Mining, San Francisco, CA, USA, May 2003.
5. Kaufman L., and Rousseeuw P.J. Finding Groups in Data: An Introduction to Cluster Analysis (1990), Wiley, New York
6. Ng R.T., and Han J. CLARANS: a method for clustering objects for spatial data mining. IEEE Trans. Knowl. Data Eng. 14 5 (2002) 1003-1016
7. Zhang T., Ramakrishnan R., and Linvy M. BIRCH: an efficient data clustering method for very large data sets. Data Mining Knowl. Discovery 1 2 (1997) 141-182
8. M. Ester, H.-P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial data sets with noise, in: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, OR, pp. 226-231.
9. A. Hinneburg, D.A. Keim, An efficient approach to clustering in large multimedia databases with noise, in: Knowledge Discovery and Data Mining, 1998.
10. Karypis G., Han E.H., and Kumar V. CHAMELEON: a hierarchical clustering algorithm using dynamic modeling. Computer 32 8 (1999) 68-75
11. Jain A.K., Murty M.N., and Flyn P.J. Data clustering: a review. ACM Comput. Surveys 31 3 (1999)
12. Ismail M.A. Soft clustering: algorithms and validity of solutions. In: Gupta M.M., and Yamakawa T. (Eds). Fuzzy Computing: Theory, Hardware and Applications (1988), Elservier Science Publishers B.V., North-Holland, Amsterdam 445-471
13. Selim S.Z., and Ismail M.A. Soft clustering of multidimensional data: a semi-fuzzy approach. Pattern Recognition 17 5 (1984) 559-568
14. Hastie T., Tibshirani R., and Friedman J. The Elements of Statistical Learning. Data Mining, Inference and Prediction (2001), Springer, New York
15. W. Wang, J. Yang, M. Muntz, STING: a statistical information grid approach to spatial data mining, in: Proceedings of the 1997 International Conference on Very Large Data Bases (VLDB'97), 1997, pp. 186-195.
16. R. Agrawal, J. Gehrke, D. Gunopulos, P. Raghavan, Automatic subspace clustering of high dimensional data for data mining applications, in: Proceedings of the 1998 ACM-SIGMOD Conference on the Management of Data, pp. 94-105.
17. G. Sheikholeslami, S. Chatterjee, A. Zhang, Wave Cluster: a multiresolution clustering approach for very large spatial databases, in: Proceedings of the 1998 International Conference on Very Large Data Bases (VLDB'98), 1998, pp. 428-439.
18. B. Qiu, X. Zhang, J. Shen, A grid based clustering algorithm for multi-density, in: Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 18-21 August 2005.
19. S. Guha, R. Rastogi, K. Shim, CURE: an efficient clustering algorithm for large databases, in: Proceedings of the 1998 ACM-SIGMOD International Conference Management of Data (SIGMOD'98), 1998, pp. 73-84.
20. S. Guha, R. Rastogi, K. Shim, ROCK: a robust clustering algorithm for categorical attributes, in: The Proceedings of the IEEE Conference on Data Engineering, 1999.
21. Zahn C. Graph-theoretical methods for detecting and describing Gestalt clusters. IEEE Trans. Comput. C-20 (1971) 68-86
22. Urquhart R. Graph theoretical clustering based on limited neighborhood set. Pattern recognition 15 3 (1982) 173-187
23. N.A. Yousri, M.A. Ismail, M.S. Kamel, Adaptive similarity search in metric trees, in: IEEE Systems Man Cybernetics (SMC) 2007, Montreal, Canada, October 2007.
24. P. Ciaccia, M. Patella, P. Zezula, Mtree: an efficient access method for similarity search in metric spaces, in: Proceedings of the 23rd Conference on Very Large Databases (VLDB'97), pp. 426-435.
25. K. Hammouda, M.S. Kamel, Phrase-based document similarity based on an index graph model, in: The 2002 IEEE International Conference on Data Mining (ICDM'02), Maebashi, Japan, December 2002, pp. 203-210.
26. Hubert L., and Arabie P. Comparing partitions. J. Classification (1985) 193-218. http://faculty.washington.edu/kayee/pca/supp.pdf (supplementary http://faculty.washington.edu/kayee/pca/supp.pdf)
27. M. Halkidi, Y. Batistakis, M. Vazirgiannis, Cluster validity methods. Part 1, SIGMOD Record 31(2), June 2002.
28. 〈http://www.math.tau.ac.il/_rshamir/click/click.html〉.
29. E. Keogh, X. Xi, L. Wei, C.A. Ratanamahatana, The UCR Time Series Classification/Clustering Homepage: 〈www.cs.ucr.edu/~eamonn/time_series_data/〉, 2006.
30. J.A. Hartigan, Clustering Algorithms, Wiley Series in Probability and Mathematical Statistics, 1975.
31. 〈http://mlearn.ics.uci.edu/MLSummary.html〉.
32. Hammouda K., and Kamel M.S. Efficient phrase-based document indexing for web document clustering. IEEE Trans. Knowl. Data Eng. (TKDE) 16 10 (2004)
33. N.A. Yousri, M.A. Ismail, M.S. Kamel, Discovering connected patterns in gene expression arrays, in: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) 2007, Hawaii, USA.
34. N.A. Yousri, M.S., Kamel, M.A. Ismail, Pattern cores and connectedness in cancer gene expression, in: Seventh IEEE International Conference on BioInformatics and BioEngineering (BIBE) 2007, Boston, USA, October 2007.
35. Kotsiantis S., and Pintelas P. Recent advances in clustering: a brief survey. WSEAS Trans. Inf. Sci. Appl. 1 (2004) 73-81
36. Xu R., and Wunsch D. Survey of clustering algorithms. IEEE Trans. Neural Networks 16 3 (2005)