GraphZip: A fast and automatic compression method for spatial data clustering

Proceedings of the ACM Symposium on Applied Computing

Volumn 1, Issue , 2004, Pages 571-575

  Qian, Yu ^a   Zhang, Kang ^a  

^a The University of Texas at Dallas   (United States)

Author keywords

Clustering; Data compression; Spatial databases

Indexed keywords

ALGORITHMS; BENCHMARKING; DATA ACQUISITION; DATA MINING; DATABASE SYSTEMS; GRAPH THEORY; HIERARCHICAL SYSTEMS; INFORMATION ANALYSIS; MATHEMATICAL TRANSFORMATIONS; TREES (MATHEMATICS);

CLUSTERING ALGORITHMS; HIERARCHIAL COMBINATION; SPATIAL DATA CLUSTERING; SPATIAL DATABASES;

DATA COMPRESSION;

EID: 2442434704     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. Bentley, J. L. (1975). Multidimensional Binary Search Trees Used for Associative Searching. Communications of the ACM 18(9), pp. 509-517.
2. th Annual Symposium on Foundations of Computer Science, pp. 226-232.
3. Ertoz, L., Steinbach, M., and Kumar, V. (2003), Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data, Proc. of SIAM DM03.
4. Ester, M., Kriegel, H. P., Sander, J., and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise, Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD-96), AAAI Press, pp. 226-231.
5. th Int'l Conf. on Geocomputation, Geo Computation CD-ROM: GC049, ISBN 0-9533477-2-9.
6. Guha, S., Rastogi, R., and Shim, K. (1998). CURE: An efficient clustering algorithm for large databases, Proc. 1998 ACM SIGMOD Int. Conf. Management of Data, pp. 73-84.
7. Guha, S., Rastogi, R., and Shim, K. (1999). ROCK: a robust clustering algorithm for categorical attributes. Proc. 1999 Int. Conf. on Data Eng., pp. 512-521.
8. Han, J. and Kamber, M. (2000). Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers.
9. Han, J., Kamber, M., and Tung, A. K. H. (2001). Spatial clustering methods in data mining: A survey, H. Miller and J. Han (eds.), Geographic Data Mining and Knowledge Discovery, Taylor and Francis.
10. th Int'l Conf. Knowledge Discovery and Data Mining (KDD-2001), ACM Press, New York, pp. 281-286.
11. Karypis, G., Han, E., and Kumar, V. (1999). CHAMELEON, A hierarchical clustering algorithm using dynamic modeling, IEEE Computer, Vol.32, pp. 68-75.
12. Koperski, K., Han, J., and Adhikari, J. (1998). Mining Knowledge in Geographical Data. Communications of the ACM, 26(1), pp. 65-74.
13. McCallum, A. Nigam, K., and Ungar, L. (2000). Efficient clustering of high-dimensional data sets with application to reference matching. Proc. of KDD2000, ACM Press, pp. 169-178.
14. McQueen, J. (1967). Some methods for classification and analysis of multivariate observations, In Proc. of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp.281-297.
15. Moore, A. (1999). Very fast EM-based mixture model clustering using multiresolution kd-trees. Advances in Neural Information Processing Systems, Vol 11 (1999), Morgan Kaufman, pp. 543-549.
16. Ng, R., and Han, J. (1994). Efficient and effective clustering method for spatial data mining. Proc. 1994 Int. Conf. Very Large Data Bases (VLDB94), pp. 281-297.
17. Smid, M. (1997). Closest-Point Problems in Computational Geometry. Handbook on Computational Geometry. J. R. Sack and J. Urrutia, editors, Elsevier Science Publishing, pp. 877-935.
18. Vaidya, P. M., (1989). An O(nlogn) algorithm for the allnearest-neighbors problem. Discrete & Computational Geometry 4 (1989), pp. 101-115.
19. Wang, W., Yang, J., and Muntz, R. (1997). STING: A Statistical Information Grid Approach to Spatial Data Mining, Proc. of the 23rd Int. Conf. on Very Large Data Bases, Morgan Kaufmann, pp. 186-195.
20. Xu, X., Ester, M., Kriegel, H. P., and Sander, J. (1997). Clustering and knowledge discovery in spatial databases. Vistas in Astronomy, 41(1997), pp. 397-403.
21. Zahn, C. T. (1971) Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters, IEEE Trans. Comput. C-20 (Apr. 1971), pp. 68-86.
22. Zhang, T., Ramakrishnan, R., and Linvy, M. (1996). BIRCH: an efficient data clustering method for very large databases, Proc. ACM SIGMOD Int'l Conf. on Management of Data, ACM Press, pp. 103-114.