K-Harmonic means data clustering with tabu-search method

Applied Mathematical Modelling

Volumn 32, Issue 6, 2008, Pages 1115-1125

  Güngör, Zülal ^a   Ünler, Alper ^b  

^a GAZI UNIVERSITY   (Turkey)

^b KKK Per Bsk ^*  (Turkey)

Author keywords

Clustering; Fuzzy K means; K Harmonic means; K means; Tabu search

Indexed keywords

COMPUTATIONAL COMPLEXITY; DATA MINING; DATA REDUCTION; TABU SEARCH;

DATA CLUSTERING; K-HARMONIC MEANS CLUSTERING;

CLUSTERING ALGORITHMS;

CLUSTERING ALGORITHMS; COMPUTATIONAL COMPLEXITY; DATA MINING; DATA REDUCTION; TABU SEARCH;

EID: 39349118272     PISSN: 0307904X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apm.2007.03.011     Document Type: Article

References (25)

1. Forgy E.W. Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics 21 3 (1965) 768-769
2. B. Zhang, M. Hsu, U. Dayal, K-harmonic means - a data clustering algorithm, Technical Report HPL-1999-124, Hewlett-Packard Laboratories, 1999.
3. B. Zhang, M. Hsu, U. Dayal, K-harmonic means, in: International Workshop on Temporal, Spatial and Spatio-Temporal Data Mining, TSDM2000, Lyon, France, 12 September 2000.
4. Garey M.R., Johnson D.S., and Witsenhausen H.S. The complexity of the generalized Lloyd-max problem. IEEE Trans. Inform. Theory 28 2 (1982) 255-256
5. Tikk D., Gedeon T.D., and Wong K.W. A Feature Ranking Algorithm for Fuzzy Modeling Problems. Interpretability Issues in Fuzzy Modeling (2003), Springer-Verlag, Heidelberg pp. 176-192
6. Jain K., Murty M.N., and Flynn P.J. Data clustering: a review. ACM Comput. Surv. 31 3 (1999) 264-323
7. Bradley P.S., and Fayyad U.M. Refining initial points for K-means clustering. Proceedings of 15th International Conference on Machine Learning (1998), Morgan Kaufmann, San Francisco, CA 91-99
8. Y. Guan, A.A. Ghorbani, N. Belace, K-means+: an autonomous clustering algorithm, Pattern Recognition, submitted for publication.
9. Pelleg D., and Moore A. X-means: extending K-means with efficient estimation of the number of clusters. Proceedings of the 17th International Conference on Machine Learning (2000), Morgan Kaufmann, San Francisco, CA 727-734
10. Dunn J.C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybernet. 3 (1973) 32-57
11. Bezdek J. Pattern Recognition with Fuzzy Objective Function Algorithm (1981), Plenum Press, New York
12. G. Hammerly, C. Elkan, Alternatives to the K-means algorithm that find better clusterings, in: Proceedings of the 11th International Conference on Information and Knowledge Management, 2002, pp. 600-607.
13. Al-Sultan K.H. A tabu search approach to the clustering problem. Pattern Recog. 28 (1995) 1443-1451
14. Al-Sultan K.S., and Fedjki A.C. A tabu search-based algorithm for the fuzzy clustering problem. Pattern Recog. 30/12 (1997) 2023-2030
15. Chu S., and Roddick J. A clustering algorithm using tabu search approach with simulated annealing for vector quantization. Chin. J. Electron. 12 3 (2003) 349-353
16. M. Delgado, A. Skarmeta, H. Barbera, A tabu search approach to the fuzzy clustering problem, in: The Sixth IEEE International Conference on Fuzzy Systems, Barcelona, 1997.
17. Frnti P., Kivijrvi J., and Nevalainen O. Tabu search algorithm for codebook generation in vector quantization. Pattern Recog. 31 8 (1998) 1139-1148
18. Sung C.S., and Jin H.W. A tabu search-based heuristic for clustering. Pattern Recog. 33 (2000) 849-858
19. Wang X.Y., and Garibaldi J.M. Simulated annealing fuzzy clustering in cancer diagnosis. Informatica 29 (2005) 61-70
20. Cano J.R., Cordon O., Herrera F., and Sanchez L. A greedy randomized search procedure applied to the clustering problem as an initialization process using K-means as a local search procedure. Journal of Intelligent and Fuzzy Systems 12 3-4 (2002) 235-242
21. Franti P., and Kivijarvi J. Randomised local search algorithm for the clustering problem. Pattern Anal. Appl. 3 (2000) 358-369
22. Hansen P., and Mladenovic N. J-means: a new local search heuristic for minimum sum-of-squares clustering. Pattern Recog. 34 (2002) 405-413
23. P.M. Kanade, L.O. Hall, Fuzzy ants as a clustering concept, in: 22nd International Conference of the North American Fuzzy Information Processing Society (NAFIPS), 2003, pp. 227-232.
24. Glover F., and Laguna M. Tabu Search (1997), Kluwer, Norwell, MA
25. C.L. Blake, C.J. Merz, UCI repository of machine learning databases, 1998, http://www.ics.uci.edu/_mlearn/MLRepository.html.