Scale up center-based data clustering algorithms by parallelism

HP Laboratories Technical Report

Volumn , Issue 6, 2000, Pages

  Zhang, Bin ^a   Hsu, Meichun ^a  

^a HEWLETT PACKARD LABORATORIES   (United States)

Author keywords

Data clustering; Data mining; E.M; Expectation Maximization; K Harmonic Means; K Means; Parallel; Parallel algorithms

Indexed keywords

DATA CLUSTERING; EXPECTATION; K-HARMONIC; K-MEANS; PARALLEL;

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTER WORKSTATIONS; DATA REDUCTION; LOCAL AREA NETWORKS; ONLINE SYSTEMS; SAMPLING;

PARALLEL PROCESSING SYSTEMS;

EID: 84862447475     PISSN: None     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (24)

1. [A73] Anderberg, M. R. 1973. Cluster analysis for applications. Academic Press, New York, xiii + 35p.
2. [BFR98] Bradley, P., Fayyad, U. M., and Reina, C.A., "Scaling EM Clustering to Large Databases," MS Technical Report, 1998.
3. [BF98] Bradley, P., Fayyad, U. M., C.A., "Refining Initial Points for KM Clustering", MS Technical Report MSR-TR-98-36, May 1998.
4. [BFR98a] Bradley, P., Fayyad, U. M., and Reina, C.A., "Scaling Clustering to Large Databases", KDD98, 1998.
5. [DH72] Duda, R., Hart, P., "Pattern Classification and Scene Analysis", John Wiley & Sons, 1972.
6. [DVJCP99] DuMouchel, W., Volinsky, C., Johnson, T., Cortes, C. and Pregibon, D., "Squashing Flat Files Flatter," Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August, 1999.
7. [DLR77] Dempster, A. P., Laird, N.M., and Rubin, D.B., "Miximum Likelyhood from Incomplete Data via the EM Algorithm", Journal of the Royal Statistical Society, Series B, 39(1): 1-38, 1977.
8. [FPU96] Fayyad, U. M., Piatetsky-Shapiro, G. Smyth, P. and Uthurusamy, R., "Advances in Knowledge Discovery and Data Mining", AAAI Press 1996.
9. [F94] T. J. Fountain, "Parallel Computing: Principles and Practice," Cambridge Univ Pr 1994.
10. [GG92] Gersho & Gray, "Vector Quantization and Signal Compression", KAP, 1992
11. [KGGK94] Vipin Kumar, Ananth Grama, Anshul Gupta, George Karypis, "Introduction to Parallel Computing: Design and Analysis of Parallel Algorithms," Addison-Wesley Pub Co, 1994.
12. [KC99] Sanpawat Kantabutra and Alva L. Couch, "Parallel K-means Clustering Algorithm on NOWs," NECTEC Technical journal (www.nectec.or.th)
13. [KR90] Kaufman, L. and Rousseeuw, P. J., "Finding Groups in Data: An Introduction to Cluster Analysis", John Wiley & Sons, 1990.
14. [L84] S. P. Lloyd, "Least Squares Quantization in PCM," Technical Note, Bell Lab, 1957 and IEEE Trans. on Information Theory 1982.
15. [M67] MacQueen, J. 1967. Some methods for classification and analysis of multivariate observations. Pp. 281-297 in: L. M. Le Cam & J. Neyman [eds.] Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Vol. 1. University of California Press, Berkeley, xvii + 666 p.
16. [MA] McKenzie, P. and Alder, M., "Initializing the EM Algorithm for Use in Gaussian Mixture Modeling", The Univ. of Western Australia, Center for Information Processing Systems, Manuscript.
17. [MK97] McLachlan, G. J. and Krishnan, T., "The EM Algorithm and Extensions.", John Wiley & Sons, Inc., 1997.
18. [NetPerception] A Commercial Recommender System http://www.netperceptions.com
19. [PM99] Pelleg, D. and Moore, A, "Accelerating Exact k-means Algorithms with Geometric Reasoning", KDD-99, Proc. of the Fifth ACM SIGKDD Intern. Conf. On Knowledge Discovery and Data Mining, page 277-281.
20. [RV97] P. Resnick and H. Varian, "Recommender Systems," in CACM March 1997.
21. [RW84] Rendner, R.A. and Walker, H.F., "Mixture Densities, Maximum Likelihood and The EM Algorithm", SIAM Review, vol. 26 #2, 1984.
22. [SI84] Selim, S.Z. and Ismail, M.A., "K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality", IEEE Trans. On PAMI-6, #1, 1984.
23. [SK99] Sanpawat Kantabutra, "Parallel K-Means Clustering Algorithm on NOW", Semptember, 1999.
24. [ZRL96] Zhang, T., Ramakrishnan, R., and Livny, M., "BIRCH: an efficient data clustering method for very large databases", ACM SIGMOD Record, Vol. 25, No. 2 (June 1996), Pages 103-114 in: SIGMOD '96.