PENS: An algorithm for density-based clustering in peer-to-peer systems

ACM International Conference Proceeding Series

Volumn 152, Issue , 2006, Pages

  Li, Mei ^a   Lee, Guanling ^b   Lee, Wang Chien ^a   Sivasubramaniam, Anand ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b NATIONAL DONG HWA UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; DATA MINING; LEARNING SYSTEMS; MATHEMATICAL MODELS;

AUTONOMOUS DATA SOURCES; GLOBAL CLUSTERING MODELS; HIERARCHICAL CLUSTER ASSEMBLY; PEER DENSITY-BASED CLUSTERING (PENS);

DISTRIBUTED COMPUTER SYSTEMS;

EID: 34547380798     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1146847.1146886     Document Type: Conference Paper

References (22)

1. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In Proceedings of SIGMOD, pages 94-105, June 1998.
2. M. Ankerst, M. M. Breunig, H.-P. Kriegel, and J. Sander. OPTICS: Ordering points to identify the clustering structure. In Proceedings of SIGMOD, pages 49-60, June 1999.
3. S. Bandyopadhyay, C. Gianella, U. Maulik, H. Kargupta, K. Liu, and S. Datta. Clustering Distributed Data Streams in Peer-to-Peer Environments. Information Science Journal (In Press), 2005.
4. Y. Chawathe, S. Ramabhadran, S. Ratnasamy, A. LaMarca, S. Shenker, and J. Hellerstein. A case study in building layered DHT applications. In Proceedings of SIGCOMM, August 2005.
5. P. Cheeseman and J. Stutz. Bayesian classification (autoclass): Theory and results. In Advances in Knowledge Discovery and Data Mining, pages 153-180. AAAI/MIT Press, 1996.
6. I. S. Dhillon and D. S. Modha. A data-clustering algorithm on distributed memory multiprocessors. In Proceedings of Workshop on Large-Scale Parallel KDD Systems (in conjunction with SIGKDD), pages 245-260, August 1999.
7. R. Duda and P. Hart. Pattern classification and scene analysis. John Wiley & Sons, New York, 1973.
8. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of Knowledge Discovery in Database (KDD), pages 226-231, 1996.
9. G. Forman and B. Zhang. Distributed data clustering can be efficient and exact. SIGKDD Explorations, 2(2):34-38, 2000.
10. S. Guha, R. Rastogi, and K. Shim. CURE: An efficient clustering algorithm for large databases. In Proceedings of SIGMOD, pages 73-84, June 1998.
11. E. Januzaj, H.-P. Kriegel, and M. Pfeifle. DBDC: Density based distributed clustering. In Proceedings of International Conference on Extending Database Technology (EDBT), pages 88-105, March 2004.
12. E. L. Johnson and H. Kargupta. Collective, hierarchical clustering from distributed, heterogeneous data. In Proceedings of Workshop on Large-Scale Parallel KDD Systems (in conjunction with SIGKDD), pages 221-244, August 1999.
13. M. Li, W.-C. Lee, and A. Sivasubramaniam. Semantic small world: An overlay network for peer-to-peer search. In Proceedings of International Conference on Network Protocols (ICNP), pages 228-238, October 2004.
14. P. Linga, A. Crainiceanu, J. Gehrke, and J. Shanmugasundaram. Guaranteeing correctness and availability in P2P range indices. In Proceedings of SIGMOD, pages 323-334, 2005.
15. J. MacQueen. Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pages 281-297, 1967.
16. S. Ratnasamy, P. Francis, M. Handley, R. M. Karp, and S. Schenker. A scalable content-addressable network. In Proceedings of ACM SIGCOMM, pages 161-172, August 2001.
17. N. F. Samatova, G. Ostrouchov, A. Geist, and A. V. Melechko. RACHET: An efficient cover-based merging of clustering hierarchies from distributed datasets. Distributed and Parallel Databases, 11(2):157-180, 2002.
18. G. Sheikholeslami, S. Chatterjee, and A. Zhang. WaveCluster: A multi-resolution clustering approach for very large spatial databases. In Proceedings of VLDB, pages 428-439, August 1998.
19. I. Stoica, R. Morris, D. Karger, M. F. Kaashoek, and H. Balakrishnan. Chord: A scalable peer-to-peer lookup service for Internet applications. In Proceedings of ACM SIGCOMM, pages 149-160, August 2001.
20. W. Wang, J. Yang, and R. R. Muntz. STING: A statistical information grid approach to spatial data mining. In Proceedings of VLDB, pages 186-195, August 1997.
21. X. Xu, J. Jäger, and H.-P. Kriegel. A fast parallel clustering algorithm for large spatial databases. Data Mining and Knowledge Discovery, 3(3):263-290, 1999.
22. T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH: An efficient data clustering method for very large databases. In Proceedings of SIGMOD, pages 103-114, June 1996.