K-harmonic means data clustering with simulated annealing heuristic

Applied Mathematics and Computation

Volumn 184, Issue 2, 2007, Pages 199-209

  Güngör, Zülal ^a   Ünler, Alper ^b  

^a GAZI UNIVERSITY   (Turkey)

^b KKK PerBsk ^*  (Turkey)

Author keywords

Clustering; Fuzzy K means; K harmonic means; K means; Simulated annealing

Indexed keywords

ALGORITHMS; DATA MINING; DATA REDUCTION; DATABASE SYSTEMS; HEURISTIC METHODS; PROBLEM SOLVING; SIMULATED ANNEALING;

CLUSTERING; FUZZY K-MEANS; K-HARMONIC MEANS; K-MEANS;

HARMONIC ANALYSIS;

EID: 33846661354     PISSN: 00963003     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.amc.2006.05.166     Document Type: Article

References (34)

1. Al-Sultan K.H. A Tabu search approach to the clustering problem. Pattern Recognit. 28 (1995) 1443-1451
2. Al-Sultan K.S., and Fedjki A.C. A Tabu search-based algorithm for the fuzzy clustering problem. Pattern Recognit. 30/12 (1997) 2023-2030
3. Bezdek J. Pattern Recognition with fuzzy objective function algorithm (1981), Plenum Press, New York
4. C.L. Blake, C.J. Merz, UCI repository of machine learning databases. Available from: .
5. Bradley P.S., and Fayyad U.M. Refining initial points for K-means clustering. Proc. 15th Int. Conf. on Machine Learning (1998), Morgan Kaufman, San Francisco, CA 91-99
6. 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. J. Intell. Fuzzy Syst. 12 3-4 (2002) 235-242
7. 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
8. M. Delgado, A. Skarmeta, H. Barbera, A Tabu search approach to the fuzzy clustering problem, in: Proc. of the Sixth IEEE Int. Conf. on Fuzzy Systems, Barcelona, 1997.
9. Dunn J.C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cyber. 3 (1973) 32-57
10. Forgy E.W. Cluster analysis of multivariate data: Efficiency versus interpretability of classifications. Biometrics 21 3 (1965) 768-769
11. Frnti P., Kivijrvi J., and Nevalainen O. Tabu search algorithm for codebook generation in vector quantization. Pattern Recognit. 31 8 (1998) 1139-1148
12. Frnti P., and Kivijarvi J. Randomised local search algorithm for the clustering problem. Pattern Anal. Appl. 3 (2000) 358-369
13. 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
14. Glover F., and Laguna M. Tabu Search (1997), Kluwer, Norwell, MA
15. Y. Guan, A.A. Ghorbani, N. Belace, K-Means+: An autonomous clustering algorithm, Technical Report (TR04-164), UNB Faculty of Computer Science, 2004, Canada.
16. G. Hammerly, C. Elkan, Alternatives to the k-means algorithm that find better clusterings, in: Proc. of the 11th Int. Conf. on Information and Knowledge Management, 2002, pp. 600-607.
17. Hansen P., and Mladenovic N. J-means: A new local search heuristic for minimum sum-of-squares clustering. Pattern Recognit. 34 (2002) 405-413
18. Huang C.H., Pan J.S., Lu Z.H., Sun S.H., and Hang H.M. Vector quantization based on genetic simulated annealing. Signal Process. 81 (2001) 1513-1523
19. Jain K., Murty M.N., and Flynn P.J. Data clustering: A review. ACM Comput. Surveys 31 3 (1999) 264-323
20. P.M. Kanade, L.O. Hall, Fuzzy Ants as a Clustering Concept, in: Proc. of 22nd Int. Conf. of the North American Fuzzy Information Processing Society (NAFIPS), 2003, pp. 227-232.
21. Khan S.S., and Ahmad A. Cluster center initialization algorithm for K-means clustering. Pattern Recognit. Lett. 25 (2004) 1293-1302
22. Kirkpatrick S., Gerlatt C.D., and Vecchi M.P. Optimization by simulated annealing. Science 220 (1983) 671-680
23. Kövesi B., Boucher J.M., and Saoudi S. Stochastic K-means algorithm for vector quantization. Pattern Recognit. Lett. 22 (2001) 603-610
24. Metropolis N., Rosenbluth A.W., Teller A.H., and Teller E. Equations of state calculations by fast computing machines. J. Chem. Phys. 21 (1958) 1087-1092
25. Patane G., and Russo M. The enhanced LBG algorithm. Neural Networks 14/9 (2001) 1219-1237
26. Pelleg D., and Moore A. X-means: Extending K-means with efficient estimation of the number of clusters. Proc. of the 17th Int. Conf. on Machine Learning (2000), Morgan Kaufman, San Francisco, CA 727-734
27. Pena J.M., Lozano J.A., and Larranaga P. An empirical comparison of four initialization methods for the K-Means algorithm. Pattern Recognit. Lett. 20 (1999) 1027-1040
28. Pincus M. A Monte Carlo method for the approximation solution of certain types of constrained optimization problems. Operations Res. 18 (1970) 1225-1228
29. Qin A.K., and Suganthan P.N. Initialization insensitive LVQ algorithm based on cost-function adaptation. Pattern Recognit. 38 (2005) 773-776
30. Sung C.S., and Jin H.W. A Tabu search-based heuristic for clustering. Pattern Recognit. 33 (2000) 849-858
31. 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 176-192
32. Wang X.Y., and Garibaldi J.M. Simulated annealing fuzzy clustering in cancer diagnosis. Informatica 29 (2005) 61-70
33. B. Zhang, M. Hsu, U. Dayal, K-harmonic means - A data clustering algorithm, Technical Report HPL-1999-124, Hewlett-Packard Laboratories (1999).
34. B. Zhang, M. Hsu, U. Dayal, K-Harmonic Means, in: Proc. of International Workshop on Temporal, Spatial and Spatio-Temporal Data Mining, TSDM2000, Lyon, France, September 12 (2000).