A k-means-like algorithm for k-medoids clustering and its performance

36th International Conference on Computers and Industrial Engineering, ICC and IE 2006

Volumn , Issue , 2006, Pages 1222-1231

  Park, Hae Sang ^a   Lee, Jong Seok ^a   Jun, Chi Hyuck ^a  

^a Pohang University of Science and Technology (POSTECH)   (South Korea)

Author keywords

Clustering; K means; K medoids

Indexed keywords

CLUSTERING; CLUSTERING ANALYSIS; DISTANCE MATRICES; HOMOGENEOUS GROUP; K-MEANS; K-MEDOIDS; K-MEDOIDS CLUSTERING; PARTITIONING AROUND MEDOIDS;

INDUSTRIAL ENGINEERING; ITERATIVE METHODS;

CLUSTERING ALGORITHMS;

EID: 70350527847     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. Han, J., Kamber, M. and Tung, A. (2001). Spatial clustering methods in data mining: A survey. In Miller. H., and Han. J., eds., Geographic Data Mining and Knowledge Discovery. Taylor & Francis.
2. Hubert, L. & P. Arabie (1985). Comparing partitions. Journal of Classification. 2, 193-218.
3. Kaufman, L. and Rousseeuw, P.J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley. New York.
4. MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Berkeley. University of California Press, 1. 281-297.
5. Ng, R. and J. Han. (1994). Efficient and Effective Clustering Methods for Spatial Data Mining. Proceedings of the 20th International Conference on Very Large Databases, Santiago. Chile.
6. Vlassis, N. and Likas, A. (2002). A greedy EM algorithm for Gaussian mixture learning. Neural Processing Letters, 15(1), 77-87.
7. ftp://ftp.ics.uci.edupub, inachine-leaming-databases/.