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ISRN Artificial Intelligence
Volume 2012 (2012), Article ID 723516, 8 pages
A Vibration Method for Discovering Density Varied Clusters
Department of Computer Engineering, Islamic University of Gaza, Palestine
Received 4 August 2011; Accepted 28 August 2011
Academic Editors: Z. He and J. A. Hernandez
Copyright © 2012 Mohammad T. Elbatta et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- A. K. Jain and R. C. Dubes, Algorithm for Clustering Data, Prentice Hall, Englewood Cliffs, NJ, USA, 1998.
- B. BahmaniFirouzi, T. Niknam, and M. Nayeripour, “A new evolutionary algorithm for cluster analysis,” in Proceedings of the World Academy of Science, Engineering and Technology, vol. 36, December 2008.
- M. E. Celebi, “Effective initialization of k-means for color quantization,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '09), pp. 1649–1652, Cairo, Egypt, November 2009.
- M. B. Al-Zoubi, A. Hudaib, A. Huneiti, and B. Hammo, “New efficient strategy to accelerate k-means clustering algorithm,” American Journal of Applied Sciences, vol. 5, no. 9, pp. 1247–1250, 2008.
- M. Borodovsky and J. McIninch, “Recognition of genes in DNA sequence with ambiguities,” BioSystems, vol. 30, no. 1–3, pp. 161–171, 1993.
- J. Bezdek and N. Pal, Fuzzy models for pattern recognition, IEEE press, New York, NY, USA, 1992.
- L. Kaufman and P. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons, New York, NY, USA, 1990.
- L. Kaufman and P. J. Rousseeuw, Clustering by Means of Medoids. StatisticalData Analysis Based on the L1 Norm, Elsevier, 1987.
- G. Gan, Ch. Ma, and J. Wu, Data Clustering: Theory, Algorithms, and Applications, ASA-SIAM Series on Statistics and Applied Probability, Society for Industrial and Applied Mathematics, 2007.
- D. Defays, “An efficeint algorithm for a complete link method,” The Computer Journal, vol. 20, pp. 364–366, 1977.
- R. Sibson, “SLINK: an optimally efficent algorithm for the single link cluster method,” The Computer Journal, vol. 16, no. 1, pp. 30–34, 1973.
- G. Karypis, E. H. Han, and V. Kumar, “Chameleon: hierarchical clustering using dynamic modeling,” Computer, vol. 32, no. 8, pp. 68–75, 1999.
- T. Zhang, R. Ramakrishnan, and M. Livny, “BIRCH: an efficient data clustering method for very large databases,” ACM Special Interest Group on Management of Data, vol. 25, no. 2, pp. 103–114, 1996.
- S. Guha, R. Rastogi, and K. Shim, “Cure: an efficient clustering algorithm for large databases,” in Proceedings of the ACM International Conference on Management of Data (SIGMOD '98), L. M. Haas and A. Tiwary, Eds., pp. 73–84, ACM Press, Seattle, sWash, USA, June 1998.
- M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise,” in Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD '96), pp. 226–231, Portland, Ore, USA, 1996.
- M. Ankerst, M. M. Breunig, H. P. Kriegel, and J. Sander, “OPTICS: ordering points to identify the clustering structure,” ACM Special Interest Group on Management of Data, vol. 28, no. 2, pp. 49–60, 1999.
- W. Wang, J. Yang, and R. Muntz, “Sting: A statistical information grid approach to spatial data mining,” in Proceedings of the 23rd International Conference on Very Large Data Bases, 1997.
- R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, “Automatic subspace clustering of high dimensional data for data mining applications,” ACM Special Interest Group on Management of Data, vol. 27, no. 2, pp. 94–105, 1998.
- G. Sheikholeslami, S. Chatterjee, and A. Zhang, “WaveCluster: a multi-resolution clustering approach for very large spatial databases,” in Proceedings of the 24th International Conference on Very Large Data Bases (VLDB '98), pp. 428–439, 1998.
- R. M. Neal and G. E. Hinton, “A new view of the EMalgorithm that justifies incremental, sparse and other variants,” in Learning in Graphical Models, M. I. Jordan, Ed., pp. 355–3681, Kluwer Academic, Boston, Mass, USA, 1998.
- J. C. Bezdek, R. Ehrlich, and W. Full, “FCM: the fuzzy c-means clustering algorithm,” Computers and Geosciences, vol. 10, no. 2-3, pp. 191–203, 1984.
- T. Pei, A. X. Zhu, C. Zhou, B. Li, and C. Qin, “A new approach to the nearest-neighbour method to discover cluster features in overlaid spatial point processes,” International Journal of Geographical Information Science, vol. 20, no. 2, pp. 153–168, 2006.
- S. Roy and D. K. Bhattacharyya, “An approach to find embedded clusters using density based techniques,” Lecture Notes in Computer Science, vol. 3816, pp. 523–535, 2005.
- C. Y. Lin, C. C. Chang, and C. C. Lin, “A new density-based scheme for clustering based on genetic algorithm,” Fundamenta Informaticae, vol. 68, no. 4, pp. 315–331, 2005.
- D. Pascual, F. Pla, and J. S. Sanchez, “Non parametric local density-based clustering for multimoda overlapping distributions,” in Proceedings of the Intelligent Data Engineering and Automated Learning (IDEAL '06), pp. 671–678, Burgos, Spain, 2006.
- A. Ram, A. Sharma, A. S. Jalal, R. Singh, and A. Agrawal, “An enhanced density based spatial clustering of applications with noise,” in Proceedings of the International Advance Computing Conference (IACC '09), pp. 1475–1478, March 2009.
- B. Borach and D. K. Bhattacharya, “A clustering technique using density difference,” in Proceedings of the International Conference on Signal Processing, Communications and Networking, pp. 585–588, 2007.
- B. Borah and D. K. Bhattacharyya, “DDSC: a density differentiated spatial clustering technique,” Journal of Computers, vol. 3, no. 2, pp. 72–79, 2008.
- L. Peng, Z. Dong, and W. Naijun, “VDBSCAN: varied density based spatial clustering of applications with noise,” in Proceedings of the International Conference on Service Systems and Service Management (ICSSSM '07), pp. 528–531, Chengdu, China, June 2007.
- D. Hsu and S. Johnson, “A vibrating method based cluster reducing strategy,” in Proceedings of the 5th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD '08), pp. 376–379, Shandong, China, October 2008.
- J. H. Peter and A. Antonysamy, “Heterogeneous density based spatial clustering of application with noise,” International Journal of Computer Science and Network Security, vol. 10, no. 8, pp. 210–214, 2010.