SPARCL: Efficient and effective shape-based clustering

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2008, Pages 93-102

  Chaoji, Vineet ^a   Al Hasan, Mohammad ^a   Salem, Saeed ^a   Zaki, Mohammed J ^a  

^a Rensselaer Polytechnic Institute   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARBITRARY SHAPE; DATA MINING TASKS; DATA SETS; DENSITY-BASED; EXISTING METHOD; K-MEANS ALGORITHM; LARGE DATASETS; LINEAR SPACES; MIXTURE MODEL; ORDER OF MAGNITUDE; SCALABLE ALGORITHMS; SHAPE BASED; TIME COMPLEXITY; TWO STAGE;

MINING;

INFORMATION MANAGEMENT;

EID: 67049172175     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2008.73     Document Type: Conference Paper

References (15)

1. M. M. Breunig, H. Kriegel, R. T. Ng, and J. Sander. Lof: Identifying density-based local outliers. In ACM SIGMOD Int. Conf. On Management of Data, 2000.
2. I. S. Dhillon, Y. Guan, and B. Julis. Kernel k-means, spectral clustering and normalized cuts. In Proc. of KDD, 2004.
3. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A Density- Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In ACM SIGKDD, pages 226-231, 1996.
4. B. J. Gao, M. Ester, J.-Y. Cai, O. Schulte, and H. Xiong. The minimum consistent subset cover problem and its applications in data mining. In ACM SIGKDD, pages 310-319, 2007. (Pubitemid 350229218)
5. S. Guha, R. Rastogi, and K. Shim. CURE: an efficient clustering algorithm for large databases. In ACM SIGMOD International Conference on Management of Data, pages 73- 84, 1998. (Pubitemid 128655958)
6. A. Hinneburg and D. Keim. An efficient approach to clustering in multimedia databases with noise. In 4th Int'l Conf. on Knowledge Discovery and Data Mining, 1999.
7. A. K. Jain and R. C. Dubes. Algorithms for Clustering Data.Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1988.
8. G. Karypis, E.-H. S. Han, and V. Kumar. Chameleon: Hierarchical clustering using dynamic modeling. Computer, 32(8):68-75, 1999.
9. J. Lee and M. Verleysen. Nonlinear Dimensionality Reduction. Springer, 2007.
10. A. Y. Ng, M. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. In Proc. of NIPS, 2001.
11. A. Okabe, B. Boots, and K. Sugihara. Spatial Tessellations: Concepts and Applications of Voronoi Diagrams. John Wiley & Sons, 1992.
12. J. Shawe-Taylor and N. Cristianini. Kernel Methods for Pattern Analysis. Cambridge University Press, 2004.
13. J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):888-905, 2000.
14. L. Zelnik-Manor and P. Perona. Self-tuning spectral clustering. In 18th Annual Conference on NIPS, 2004.
15. Y. Zhao and G. Karypis. Hierarchical clustering algorithms for document datasets. Data Mining and Knowledge Discovery, 10(2):141-168, 2005. (Pubitemid 41217849)