A Projection Clustering Technique Based on Projection
Xiyu LIU, Xinjiang XIE, Wenping WANG
DOI: 10.4236/jssm.2009.24043   PDF    HTML     5,250 Downloads   8,582 Views   Citations


Projection clustering is an important cluster problem. Although there are extensive studies with proposed algorithms and applications, one of the basic computing architectures is that they are all at the level of data objects. The purpose of this paper is to propose a new clustering technique based on grid architecture. Our new technique integrates minimum spanning tree and grid clustering together. By this integration of projection clustering with grid technique, the complexity of computing is lowered to O(NLogN).

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LIU, X. , XIE, X. and WANG, W. (2009) A Projection Clustering Technique Based on Projection. Journal of Service Science and Management, 2, 362-367. doi: 10.4236/jssm.2009.24043.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] J. W. Han and M. Kamber, “Data mining concepts and techniques,” 2nd Edition, Elsevier, Singapore, 2006.
[2] M. Ester, H. P. Kriegel, J. Sander, and X. Xu, “A den-sity-based algorithm for discovering clusters in large spa-tial databases with noise,” in Proceedings 2nd Interna-tional Conference on Knowledge Discovery and Data Mining, Portland, OR, pp. 226–231, 1996.
[3] R. Bar-Or and C. van Woudenberg, “A novel grav-ity-based clustering method, technical report,” Depart-ment of Applied Mathematics, University of Colorado, Denver, CO 80202, 2001.
[4] R. Bar-Or and C. van Woudenberg, “Gene expression analysis with a novel gravity-based clustering method,” pp.1–46, December 2001.
[5] R. T. Ng and J. Han, “Clarans: A method for clustering objects for spatial data mining,” IEEE Trans. on Knowl-edge and Data Engineering, Vol. 14, No. 5, pp. 1003–1016, September/October 2002.
[6] M. Li, M. K. Ng, Y. M. Cheung, and J. Huang, “Agglom-erative fuzzy K-Means clustering algorithm with selection of number of clusters,” IEEE Trans on Knowledge and Data Engineering, Vol. 20, No. 11, pp. 1519–1534, 2008.
[7] Z. He, X. Xu, and S. Deng, “Scalable algorithms for clus-tering large datasets with mixed type attributes,” Interna-tional Journal of Intelligent Systems, Vol. 20, pp. 1077–1089, 2005.
[8] Z. F. He and F. L. Xiong, “A constrained partition model and K-Means algorithm,” Journal of Software, Vol.16, No.5, pp. 799–809, 2005.
[9] Z. Y. Zhang and H. Y. Zha, “Principal manifolds and nonlinear dimensionality reduction via tangent space alignment,” SIAM Journal of Scientific Computing, Vol. 26, No. 1, pp. 313–338, 2004.
[10] M. Bouguessa and S. R. Wang, “Mining projected clus-ters in high-dimensional spaces,” IEEE Transaction on Knowledge and Data Engineering, Vol. 21, No. 4, pp. 507–522, 2009.
[11] X. C. Wang, X. L. Wang, and D. M. Wilkes, “A di-vide-and-conquer approach for minimum spanning tree- based clustering,” IEEE Trans on Knowledge and Data Engineering, Vol. 21, No. 7, pp. 945–958, 2009.
[12] C. T. Zahn, “Graph-theoretical methods for detecting and describing gestalt clusters,” IEEE Trans. Computers, Vol. 20, No. 1, pp. 68–86, January 1971.
[13] C. H. Li and Z. H. Sun, “A mean approximation approach to a class of grid-based clustering algorithms,” Journal of Software, Vol. 14, No. 7, pp. 1267–1274, 2003.
[14] Marc-Ismael, Akodj`enou-Jeannin, Kav′e Salamatian, and P. Gallinari, “Flexible grid-based clustering,” J. N. Kok et al. (Eds.), LNAI 4702, PKDD, pp. 350–357, 2007.
[15] U. Brandes, M. Gaertler, and D. Wagner, “Experiments on graph clustering algorithms,” In ESA, pp. 568–579, 2003.

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