A scalable parallel subspace clustering algorithm for massive data sets

Proceedings of the International Conference on Parallel Processing

Volumn 2000-January, Issue , 2000, Pages 477-484

  Nagesh, H S ^a   Goil, S ^b   Choudhary, A ^a  

^a NORTHWESTERN UNIVERSITY   (United States)

^b SUN MICROSYSTEMS   (United States)

Author keywords

Clustering algorithms; Concurrent computing; Data mining; Grid computing; Large scale systems; Machine learning; Machine learning algorithms; Parallel processing; Statistics; Sun

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL COMPLEXITY; DATA MINING; GRID COMPUTING; LARGE SCALE SYSTEMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; PARALLEL PROCESSING SYSTEMS; STATISTICS; SUN;

COMMUNICATION OVERHEADS; CONCURRENT COMPUTING; DATA MINING PROBLEMS; HIGH DIMENSIONAL SPACES; MULTIDIMENSIONAL DATA; PARALLEL PROCESSING; QUALITY OF CLUSTERING; UNSUPERVISED CLUSTERING ALGORITHM;

CLUSTERING ALGORITHMS;

EID: 84949487685     PISSN: 01903918     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPP.2000.876164     Document Type: Conference Paper

References (19)

1. C. Aggarwal, C. Procopiuc, J. Wolf, P. Yu, and J. Park. A Framework for Finding Projected Clusters in High Dimensional Spaces. In Proc. ACM SIGMOD International Conference on Management of Data, 1999.
2. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In Proc. ACM SIGMOD International Conference on Management of Data, 1998.
3. C. Blake and C. Merz. UCI repository of machine learning databases. 1998. http://www.ics.uci.edu/~mlearn/MLRepository.html.
4. C. Cheng, A. Fu, and Y. Zhang. Entropy-based subspace clustering for mining numerical data. In Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1999.
5. I. Dhillon and D. Modha. A data-clustering algorithm on distributed memory multiprocessors. Large-Scale Parallel KDD Systems, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1999.
6. J. Eichenauer-Herrmann and H. Grothe. A new inversive congruential pseudorandom number generator with power of two modulus. ACM Transactions on Modeling and Computer Simulation, 2(1):1-11, January 1992.
7. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proc. of the 2nd International Conference on Knowledge Discovery in Databases and Data Mining, 1996.
8. K. Fukunaga. Introduction to Statistical Pattern Recognition. Academic Press, 1990.
9. S. Guha, R. Rastogi, and K. Shim. CURE: An Efficient Clustering Algorithm for Large Databases. In Proc. ACM SIGMOD International Conference on Management of Data, 1998.
10. A. Jain and R. Dubes. Algorithms for Clustering Data. Prentice-Hall Inc., 1988.
11. P. McJones. Digital Equipment Corporation, Systems Research Center. 1997. http://www.research.digital.com/SRC/.
12. R. Michalski and R. Stepp. Learning from Observation: Conceptual Clustering. Machine Learning: An Artificial Intelligence Approach, 1:331-363, 1983.
13. R. Ng and J. Han. Efficient and effective clustering methods for spatial data mining. Proc. 20th Int. Conf. on Very large Data Bases, Santiago, Chile, pages 144-155, 1994.
14. R. Ng and J. Han. Efficient and Effective Clustering Methods for Spatial Data Mining. In Proc. 20th International Conference on Very Large Databases, 1994.
15. C. Olson. Parallel algorithms for hierarchical clustering. Parallel Computing, 21, 1995.
16. G. Sheikholeslami, S. Chatterjee, and A. Zhang. WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases. In Proc. 24th International Conference on Very Large Databases, 1998.
17. L. Ungar and D. Foster. Clustering methods for collaborative filtering. AAAI Workshop on Recommendation Systems, 1998.
18. A. S. Weigend and H. G. Zimmermann. Exploiting local relations as soft constraints to improve forecasting. Journal of Computational Intelligence in Finance, 6:14-23, 1998.
19. T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH: An efficient data clustering method for very large databases. In Proc. ACM SIGMOD International Conference on Management of Data, 1996.