A neighborhood-based clustering algorithm

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

Volumn 3518 LNAI, Issue , 2005, Pages 361-371

  Zhou, Shuigeng ^a   Zhao, Yue ^a   Guan, Jihong ^b   Huang, Joshua ^c  

^a FUDAN UNIVERSITY   (China)

^b TONGJI UNIVERSITY   (China)

^c UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

DATA PROCESSING; DATABASE SYSTEMS; PARAMETER ESTIMATION;

ARBITRARY SHAPE; CLUSTERING ALGORITHM; INPUT PARAMETERS; NEIGHBORHOOD BASED CLUSTERING;

ALGORITHMS;

EID: 26944448939     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11430919_43     Document Type: Conference Paper

References (5)

1. J. Han and M. Kamber. Data mining: concepts and techniques. Morgan Kaufmann Publishers, 2000.
2. Ester M., Kriegel H., Sander J., and Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. KDD'96, pages 226-231, Portland, Oregon, 1996.
3. R. Weber, H.-J. Schek and S. Blott. A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In Proc. of VLDB'98, pages 194-205, New York City, NY, August 1998.
4. M. Stonebraker, J. Frew, K. Gardels, and J. Meredith. The SEQUOIA 2000 Storage Benchmark. In Proc. of SIGMOD'93, pages 2-11, Washington B.C., 1993.
5. C. Merz, P. Murphy, and D. Aha. UCI Repository of Machine Learning Databases. At http://www.ics.uci.edu/mlearn/MLRepository.html.