Ranked k-medoids: A fast and accurate rank-based partitioning algorithm for clustering large datasets

Knowledge-Based Systems

Volumn 39, Issue , 2013, Pages 133-143

  Razavi Zadegan, Seyed Mohammad ^a   Mirzaie, Mehdi ^b,c   Sadoughi, Farahnaz ^a  

^a TEHRAN UNIVERSITY OF MEDICAL SCIENCES   (Iran)

^b SHAHID BEHESHTI UNIVERSITY OF MEDICAL SCIENCES   (Iran)

^c INSTITUTE FOR RESEARCH IN FUNDAMENTAL SCIENCES IPM   (Iran)

Author keywords

Clustering analysis; External validation measures; k Harmonic means; k Medoids clustering; Partitioning clustering

Indexed keywords

CLUSTERING ANALYSIS; EXTERNAL VALIDATION MEASURES; K-HARMONIC MEANS; K-MEDOIDS; PARTITIONING CLUSTERING;

AXIAL FLOW; ITERATIVE METHODS;

CLUSTERING ALGORITHMS;

EID: 84871924112     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2012.10.012     Document Type: Article

References (41)

1. J. Han, M. Kamber, and J. Pei Data Mining: Concepts and Techniques 2006 Morgan Kaufmann
2. P.C. Chang, C.H. Liu, and C.Y. Fan Data clustering and fuzzy neural network for sales forecasting: a case study in printed circuit board industry Knowledge-Based Systems 22 5 2009 344 355
3. E. Hadavandi, H. Shavandi, and A. Ghanbari Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting Knowledge-Based Systems 23 8 2010 800 808
4. V. Subramanyam Rallabandi, and S. Sett Knowledge-based image retrieval system Knowledge-Based Systems 21 2 2008 89 100
5. M. ElAlami Supporting image retrieval framework with rule base system Knowledge-Based Systems 24 2 2011 331 340
6. J.D. Martin-Guerrero, A. Palomares, E. Balaguer-Ballester, E. Soria-Olivas, J. Gomez-Sanchis, and A. Soriano-Asensi Studying the feasibility of a recommender in a citizen web portal based on user modeling and clustering algorithms Expert Systems with Applications 30 2 2006 299 312
7. J. Ponomarenko, T. Merkulova, G. Orlova, O. Fokin, E. Gorshkov, and M. Ponomarenko Mining DNA sequences to predict sites which mutations cause genetic diseases Knowledge-Based Systems 15 4 2002 225 233
8. J. Shi, and Z. Luo Nonlinear dimensionality reduction of gene expression data for visualization and clustering analysis of cancer tissue samples Computers in Biology and Medicine 40 8 2010 723 732
9. D. Sebiskveradze, V. Vrabie, C. Gobinet, A. Durlach, P. Bernard, E. Ly, M. Manfait, P. Jeannesson, and O. Piot Automation of an algorithm based on fuzzy clustering for analyzing tumoral heterogeneity in human skin carcinoma tissue sections Laboratory Investigation 91 5 2011 799 811
10. S. Kalyani, and K. Swarup Particle swarm optimization based K-means clustering approach for security assessment in power systems Expert Systems with Applications 38 9 2011 10839 10846
11. T. Zhang, R. Ramakrishnan, M. Livny, BIRCH: an efficient data clustering method for very large databases, in: ACM SIGMOD Conf. Management of Data, 1996.
12. G. Karypis, E.H. Han, and V. Kumar Chameleon: hierarchical clustering using dynamic modeling Computer 32 8 1999 68 75
13. L. Kaufman, and P.J. Rousseeuw Finding Groups in Data: An Introduction to Cluster Analysis vol. 39 1990 Wiley Online Library
14. J. Sander, M. Ester, H.P. Kriegel, and X. Xu Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications Data Mining and Knowledge Discovery 2 2 1998 169 194
15. C. Fraley, and A.E. Raftery Model-based clustering, discriminant analysis, and density estimation Journal of the American Statistical Association 97 458 2002 611 631
16. J. Vesanto, and E. Alhoniemi Clustering of the self-organizing map IEEE Transactions on Neural Networks 11 3 2000 586 600
17. W. Wang, J. Yang, R. Muntz, STING: a statistical information grid approach to spatial data mining, in: The International Conference on Very Large Databases, 1997.
18. R. Xu, and D. Wunsch Survey of clustering algorithms IEEE Transactions on Neural Networks 16 3 2005 645 678
19. J. MacQueen, Some methods for classification and analysis of multivariate observations, in: The 5th Berkeley Symposium Math, Statistic and Probability, Berkeley, CA, 1967.
20. F. Yang, T. Sun, and C. Zhang An efficient hybrid data clustering method based on K-harmonic means and particle swarm optimization Expert Systems with Applications 36 6 2009 9847 9852
21. J.C. Bezdek, and R. Ehrlich FCM: the fuzzy c-means clustering algorithm Computers and Geosciences 10 2-3 1984 191 203
22. B. Zhang, M. Hsu, U. Dayal, K-harmonic means-a data clustering algorithm, Hewllet-Packard Research Laboratory Technical Report HPL-1999-124, 1999.
23. U. Maulik, and S. Bandyopadhyay Genetic algorithm-based clustering technique Pattern recognition 33 9 2000 1455 1465
24. Y.T. Kao, E. Zahara, and I. Kao A hybridized approach to data clustering Expert Systems with Applications 34 3 2008 1754 1762
25. H. Jiang, S. Yi, J. Li, F. Yang, and X. Hu Ant clustering algorithm with K-harmonic means clustering Expert Systems with Applications 37 12 2010 8679 8684
26. J. Senthilnath, S. Omkar, and V. Mani Clustering using firefly algorithm: performance study Swarm and Evolutionary Computation 1 3 2011 164 171
27. C. Zhang, D. Ouyang, and J. Ning An artificial bee colony approach for clustering Expert Systems with Applications 37 7 2010 4761 4767
28. K.E. Parsopoulos, M.N. Vrahatis, Particle Swarm Optimization and Intelligence: Advances and Applications, Information Science Reference, New York, 2010.
29. H.S. Park, and C.H. Jun A simple and fast algorithm for K-medoids clustering Expert Systems with Applications 36 2 2009 3336 3341
30. Q. Zhang, and I. Couloigner A new and efficient K-medoid algorithm for spatial clustering Computational Science and Its Applications-ICCSA 2005 2005 207 224
31. R.T. Ng, and J. Han Clarans: a method for clustering objects for spatial data mining IEEE Transactions on Knowledge and Data Engineering 14 5 2002 1003 1016
32. Y. Tian, D. Liu, H. Qi, K-harmonic means data clustering with differential evolution, in: International Conference on Future BioMedical Information Engineering, 2009.
33. Clustering Datasets. < http://cs.joensuu.fi/sipu/datasets > (retrieved 01.08.12).
34. P. Franti, and O. Virmajoki Iterative shrinking method for clustering problems Pattern Recognition 39 5 2006 761 775
35. UCI Machine Learning Repository, 2010. < http://archive.ics.uci.edu/ ml/datasets.html > (retrieved 01.08.12).
36. R.M. Aliguliyev Performance evaluation of density-based clustering methods Information Sciences 179 20 2009 3583 3602
37. B.G. Mirkin Mathematical Classification and Clustering vol. 11 1996 Springer
38. J. Wu, J. Chen, H. Xiong, and M. Xie External validation measures for K-means clustering: a data distribution perspective Expert Systems with Applications 36 3 2009 6050 6061
39. D. Steinley Properties of the Hubert-Arable adjusted rand index Psychological Methods 9 3 2004 386
40. K.Y. Yeung, and W.L. Ruzzo Details of the adjusted rand index and clustering algorithms, supplement to the paper "An empirical study on principal component analysis for clustering gene expression data" Bioinformatics 17 9 2001 763 774
41. C. Veenhuis, and M. Köppen Data swarm clustering Swarm Intelligence in Data Mining 34 2006 221 241