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MC-SC2 Publications about: k-means
Articles in journal or book chapters
  1. K.-L. Chung and K.-S. Lin. An efficient line symmetry-based k-means algorithm. Pattern Recognition Letters, 27(7):765-772, 2006. Note: Short Communication. Keyword(s): clustering, k-means algorithm, point symmetry, line symmetry. [bibtex-entry]

  2. C. Cifarelli, L. Nieddu, O. Seref, and P. Pardalos. K-T.R.A.C.E: A kernel k-means procedure for classification. Computers & Operations Research, 2006. Note: In Press. Keyword(s): classification, k-means, kernel functions. [bibtex-entry]

  3. K. Doherty, R. Adams, and N. Davey. Unsupervised learning with normalised data and non-Euclidean norms. Applied Soft Computing, 2006. Note: In Press. Keyword(s): distance measures, data normalisation, unsupervised learning, neural gas, growing neural gas, self-organising map, k-means. [bibtex-entry]

  4. U. Gonzales-Barron and F. Butler. A comparison of seven thresholding techniques with the k-means clustering algorithm for measurement of bread-crumb features by digital image analysis. Journal of Food Engineering, 74(2):268-278, 2006. Keyword(s): image analysis, bread, thresholding, crumb features. [bibtex-entry]

  5. Z. Knops, J. Maintz, M. Viergever, and J. Pluim. Normalized mutual information based registration using k-means clustering and shading correction. Medical Image Analysis, 10(3):432-439, 2006. Keyword(s): image registration, normalized mutual information, k-means clustering, shading correction, robustness. [bibtex-entry]

  6. H. Lee, H. Chen, and J. Lin. K-means method for rough classification of R&D employees' performance evaluation. International Transactions in Operational Research, 13(4):365-377, 2006. [bibtex-entry]

  7. S. Mingoti and J. Lima. Comparing SOM neural network with fuzzy c-means, k-means and traditional hierarchical clustering algorithms. European Journal of Operational Research, 2006. Note: In Press. Keyword(s): multivariate statistics, hierarchical clustering, SOM neural network, fuzzy c-means, k- means. [bibtex-entry]

  8. M. Otsubo, K. Sato, and A. Yamaji. Computerized identification of stress tensors determined from heterogeneous fault-slip data by combining the multiple inverse method and k-means clustering. Journal of Structural Geology, 28(6):991-997, 2006. Keyword(s): stress tensor, k-means, multiple inverse method, s-space, stress difference, meso-scale fault. [bibtex-entry]

  9. G. Papamichail and D. Papamichail. The k-means range algorithm for personalized data clustering in e-commerce. European Journal of Operational Research, 2006. Note: In Press. Keyword(s): heuristics, distributed consumer decision-making, range search, data clustering, personalized systems. [bibtex-entry]

  10. G. Peters. Some refinements of rough k-means clustering. Pattern Recognition, 39(8):1481-1491, 2006. Keyword(s): cluster algorithms, rough k-means, soft computing, data analysis, forest data, bioinformatics data. [bibtex-entry]

  11. R.-J. Kuo, H.-S. Wang, T.-L. Hu, and S.-H. Chou. Application of ant k-means on clustering analysis. Computers & Mathematics with Applications, 50(10-12):1709-1724, 2005. Keyword(s): data mining, clustering analysis, ant colony optimization. [bibtex-entry]

  12. Y. Marzouk and A. Ghoniem. K-means clustering for optimal partitioning and dynamic load balancing of parallel hierarchical N-body simulations. Journal of Computational Physics, 207(2):493-528, 2005. Keyword(s): k-means clustering, treecode, n-body problems, hierarchical methods, parallel processing, load balancing, particle methods, vortex methods, three-dimensional flow, transverse jet. [bibtex-entry]

  13. J. Tian, L. Zhu, S. Zhang, and L. Liu. Improvement and parallelism of k-means clustering algorithm. Tsinghua Science & Technology, 10(3):277-281, 2005. Keyword(s): data mining, cluster analysis, k-means algorithm, parallelism. [bibtex-entry]

  14. Y. De Smet and L. Montano Guzmán. Towards multicriteria clustering: An extension of the k-means algorithm. European Journal of Operational Research, 158(2):390-398, 2004. Keyword(s): multiple criteria analysis, clustering, preference modelling, k-means algorithm. [bibtex-entry]

  15. T. Kanungo, D. Mount, N. Netanyahu, C. Piatko, R. Silverman, and A. Wu. A local search approximation algorithm for k-means clustering. Computational Geometry, 28(2-3):89-112, 2004. Keyword(s): clustering, k-means, approximation algorithms, local search, computational geometry. [bibtex-entry]

  16. S. Khan and A. Ahmad. Cluster center initialization algorithm for k-means clustering. Pattern Recognition Letters, 25(11):1293-1302, 2004. Note: Short Communication. Keyword(s): k-means clustering, initial cluster centers, cost function, density based multiscale data condensation. [bibtex-entry]

  17. Y.-M. Cheung. K*-means: A new generalized k-means clustering algorithm. Pattern Recognition Letters, 24(15):2883-2893, 2003. Note: Short Communication. Keyword(s): clustering analysis, k-means algorithm, cluster number, rival penalization. [bibtex-entry]

  18. A. Likas, N. Vlassis, and J. Verbeek. The global k-means clustering algorithm. Pattern Recognition, 36(2):451-461, 2003. Keyword(s): clustering, k-means algorithm, global optimization, k-d trees, data mining. [bibtex-entry]

  19. D. Modha and W. Spangler. Feature weighting in k-means clustering. Machine Learning, 52(3):217-237, 2003. Keyword(s): clustering, convexity, convex k-means algorithm, feature combination, feature selection, Fisher’s discriminant analysis, text mining, unsupervised learning. [bibtex-entry]

  20. M. Vrahatis, B. Boutsinas, P. Alevizos, and G. Pavlides. The new k-windows algorithm for improving the k-means clustering algorithm. Journal of Complexity, 18(1):375-391, 2002. Keyword(s): k-means clustering algorithm, unsupervised learning, data mining, range search. [bibtex-entry]

  21. M. Vichi and H. Kiers. Factorial k-means analysis for two-way data. Computational Statistics & Data Analysis, 37(1):49-64, 2001. Keyword(s): cluster analysis, factorial model, tandem analysis, k-means algorithm. [bibtex-entry]

  22. M. Ng. A note on constrained k-means algorithms. Pattern Recognition, 33(3):515-519, 2000. Keyword(s): clustering, constraints, k-means algorithm, PCB insertion. [bibtex-entry]

  23. H. Hruschka and M. Natter. Comparing performance of feedforward neural nets and k-means for cluster-based market segmentation. European Journal of Operational Research, 114(2):346-353, 1999. Keyword(s): neural networks, marketing, k-means, cluster analysis, market segmentation. [bibtex-entry]

  24. P. Lagacherie, D. Cazemier, P. van Gaans, and P. Burrough. Fuzzy k-means clustering of fields in an elementary catchment and extrapolation to a larger area. Geoderma, 77(2-4):197-216, 1997. Keyword(s): hydrology, fuzzy sets, clustering. [bibtex-entry]

  25. P. Lagacherie, D. Cazemier, P. van Gaans, and P. Burrough. Fuzzy k-means clustering of fields in an elementary catchment and extrapolation to a larger area. Geoderma, 77(2-4):197-216, 1997. Keyword(s): hydrology, fuzzy sets, France. [bibtex-entry]



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Last modified: Fri Dec 15 07:05:48 2006
Author: Juscelino Dias.

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