High-dimensional shared nearest neighbor clustering algorithm

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3614 LNAI, Issue , 2006, Pages 494-502

  Yin, Jian ^a   Fan, Xianli ^a   Chen, Yiqun ^a,b   Ren, Jiangtao ^a  

^a SUN YAT SEN UNIVERSITY   (China)

^b GUANGDONG UNIVERSITY OF EDUCATION   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; DATA STRUCTURES; MATHEMATICAL MODELS; STATISTICAL METHODS;

CLUSTERING ALGORITHM; CORE POINTS; SHARED NEAREST NEIGHBOR (SNN);

DATABASE SYSTEMS;

EID: 33749023876     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (13)

1. Clarke, F., Ekeland, I.: Nonlinear oscillations and boundary-value problems for Hamiltonian systems. Arch. Rat. Mech. Anal. 78 (1982) 315-333
2. S. Guha, R. Rastogi, K. Shim. Cure: An efficient clustering algorithm for large databases. In Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data(SIGMOD'98). (1998)73-84.
3. Levent Ertoz, Michael Steinbach, Vipin Kumar. Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data. In Proceedings of Third SIAM International Conference on Data Mining, San Francisco, CA, USA, May 2003.
4. Levent Ertoz, Michael Steinbach, Vipin Kumar. A New Shared Nearest Neighbor Clustering Algorithm and its Applications. Workshop on Clustering High Dimensional Data and its Applications, Second SIAM International Conference on Data Mining, Arlington, VA, USA, 2002.
5. Stephen D. Bay, Mark Schwabacher. Mining Distance-Based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule. In Conference on Knowledge Discovery in Data archive Proceedings of the ninth ACM SIGKDD International Conference (KDD). (2003)29-38.
6. E. Eskin, A. Arnold, M. Prerau, L. Portnov, S. Stolfo. A framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data. Applications of Data Mining in Computer Security, number 10/320,259, filing date: December 16, Kluwer 2002.
7. A. Strehl, J. Ghosh, R. Mooney. Impact of Similarity Measures on Web-page Clustering. In Proceedings of the 17th National Conference on Artificial Intelligence: Workshop of Artificial Intelligence for Web Search, AAAI/MIT Press. (2000)58-64.
8. P.-N. Tan, M. Steinbach, V. Kumar, S. Klooster, C. Potter, and A. Torregrosa. Finding spatio-termporal patterns in earth science data. In KDD Temporal Data Mining Workshop, San Francisco, California, USA, August 2001.
9. M. Steinbach, P.-N. Tan, V. Kumar, S. Klooster, and C. Potter. Temporal data mining for the discovery and analysis of ocean climate indices. In Proceedings of the KDD Temporal Data Mining Workshop, Edmonton, Alberta, Canada, August 2002.
10. S. Shekhar, C. T. Lu, S. Chawla, P. Zhang. Data Mining and Visualization of Twin-Cities Traffic Data. University of Minnesota Academic report, 2001.
11. Vipin Kumar, Michael Steinbach, Pang-Ning Tan. Mining Scientific Data: Discovery of Patterns in the Global Climate System. PAKDD, May 7, 2002.
12. Kitamoto Asanobu. Data mining for Typhoon Image Collection. Journal of Intelligent Information Systems,(2002)Vol.19, No.1.25-41.
13. P.-N. Tan, M. Steinbach, V. Kumar, S. Klooster, C. Potter, and A. Torregrosa. Finding spatio-termporal patterns in earth science data. In KDD Temporal Data Mining Workshop, San Francisco, California, USA, August 2001.