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MC-SC2 Publications about: clustering
Books and proceedings
  1. K. Jajuga, A. Sokolowski, and H.-H. Bock, editors. Classification, Clustering and Data Analysis: Recent Advances and Applications, Studies in Classification, Data Analysis & Knowledge Organization. Springer-Verlag, Berlin, 2002. [bibtex-entry]


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. Eick, A. Rouhana, A. Bagherjeiran, and R. Vilalta. Using clustering to learn distance functions for supervised similarity assessment. Engineering Applications of Artificial Intelligence, 19(4):395-401, 2006. Keyword(s): distance function learning, supervised clustering, nearest neighbor. [bibtex-entry]


  3. 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]


  4. 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]


  5. 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]


  6. 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]


  7. 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]


  8. 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]


  9. L. Rueda and Y. Zhang. Geometric visualization of clusters obtained from fuzzy clustering algorithms. Pattern Recognition, 39(8):1415-1429, 2006. Keyword(s): fuzzy clustering, fuzzy a-means, cluster visualization, expectation maximization. [bibtex-entry]


  10. Y. Yang and M. Kamel. An aggregated clustering approach using multi-ant colonies algorithms. Pattern Recognition, 39(7):1278-1289, 2006. Keyword(s): ant algorithm, multi-ant colonies, clustering, aggregated clustering. [bibtex-entry]


  11. A. Goktepe, S. Altun, and A. Sezer. Soil clustering by fuzzy c-means algorithm. Advances in Engineering Software, 36(10):691-698, 2005. Keyword(s): fine grained soils, fuzzy c-means, hard k-means, clustering. [bibtex-entry]


  12. 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]


  13. 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]


  14. 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]


  15. C.-A. Tsai, T.-C. Lee, I.-C. Ho, U.-C. Yang, C.-H. Chen, and J. Chen. Multi-class clustering and prediction in the analysis of microarray data. Mathematical Biosciences, 193(1):79-100, 2005. Keyword(s): bagged clustering, bagging fuzzy clustering, gene selection, k-nn classification, rand statistic, shaded similarity matrix plot. [bibtex-entry]


  16. N. Belacel, M. Cuperlovic-Culf, M. Laflamme, and R. Ouelette. Fuzzy J-means and VNS methods for clustering genes from microarray data. Bioinformatics Journal, 20:1690-1701, 2004. [bibtex-entry]


  17. 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]


  18. 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]


  19. 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]


  20. J. Sarkis and S. Talluri. Performance based clustering for benchmarking of US airports. Transportation Research Part A: Policy and Practice, 38(5):329-346, 2004. Keyword(s): data envelopment analysis, clustering, benchmarking, performance analysis. [bibtex-entry]


  21. W.-J. Wang, Y.-X. Tan, J.-H. Jiang, J.-Z. Lu, G.-L. Shen, and R.-Q. Yu. Clustering based on kernel density estimation: nearest local maximum searching algorithm. Chemometrics and Intelligent Laboratory Systems, 72(1):1-8, 2004. Keyword(s): NLMSA, pattern recognition, cluster analysis, kernel density estimation, local optimization. [bibtex-entry]


  22. 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]


  23. 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]


  24. 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]


  25. T. Tarpey and K. Kinateder. Clustering functional data. Journal of Classification, 20:93-114, 2003. Keyword(s): Fourier basis, Gaussian random functions, k-means algorithm, mean squared error, principal components, principal points. [bibtex-entry]


  26. N. Belacel, P. Hansen, and N. Mladenovic. Fuzzy J-means: A new heuristic for fuzzy clustering. Pattern Recognition, 35(10):2193-2200, 2002. Keyword(s): unsupervised classification, fuzzy clustering, local search, fuzzy c-means, variable neighbourhood search. [bibtex-entry]


  27. R. Bisdorff. ELECTRE-like clustering from a pairwise fuzzy proximity index. European Journal of Operational Research, 138(2):320-331, 2002. Keyword(s): multiple criteria analysis, fuzzy clustering, graph theory. [bibtex-entry]


  28. A. Nola, V. Loia, and A. Staiano. An evolutionary approach to spatial fuzzy c-means clustering. Fuzzy Optimization and Decision Making, 1:195-219, 2002. Keyword(s): clustering algorithm, fuzzy c-means, fuzzy sets, genetic algorithm, Java language. [bibtex-entry]


  29. F. Questier, I. Arnaut-Rollier, B. Walczak, and D. Massart. Application of rough set theory to feature selection for unsupervised clustering. Chemometrics and Intelligent Laboratory Systems, 63:155-167, 2002. Keyword(s): rough sets, unsupervised clustering, feature selection, Wallace measure. [bibtex-entry]


  30. 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]


  31. V. Ananthanarayana, M. Murty, and D. Subramanian. Efficient clustering of large data sets. Pattern Recognition, 34(12):2561-2563, 2001. Note: Short Communication. [bibtex-entry]


  32. V. Ananthanarayana, M. Murty, and D. Subramanian. Multi-dimensional semantic clustering of large databases for association rule mining. Pattern Recognition, 34(4):939-941, 2001. Note: Short Communication. [bibtex-entry]


  33. U. Maulik and S. Bandyopadhyay. Genetic algorithm-based clustering technique. Pattern Recognition, 33(9):1455-1465, 2000. Keyword(s): genetic algorithm, clustering metric, k-means algorithm, real encoding, Euclidean distance. [bibtex-entry]


  34. 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]


  35. V. Chepoi and D. Dumitrescu. Fuzzy clustering with structural constraints. Fuzzy Sets and Systems, 105:91-97, 1999. Keyword(s): cluster analysis, fuzzy n-means algorithm with structural constraints, multifacility location problem, structure graph. [bibtex-entry]


  36. Y. Shapira and I. Gath. Feature selection for multiple binary classification problems. Pattern Recognition Letters, 20:823-832, 1999. Keyword(s): feature selection, transpose projection, alternative partitions, clustering. [bibtex-entry]


  37. 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]


  38. 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]


  39. Y. Won and S. Kim. Multiple criteria clustering algorithm for solving the group technology problem with multiple process routings. Computers & Industrial Engineering, 32(1):207-220, 1997. [bibtex-entry]


  40. M. Al-Daoud and S. Roberts. New methods for the initialisation of clusters. Pattern Recognition Letters, 17(5):451-455, 1996. Keyword(s): clustering, cluster initialisation, k-means algorithm. [bibtex-entry]


  41. P. Rousseeuw, L. Kaufman, and E. Trauwaert. Fuzzy clustering using scatter matrices. Computational Statistics & Data Analysis, 23(1):135-151, 1996. Keyword(s): ellipsoidal clusters, fuzzy clustering, industrial applications, maximum likelihood, SAND method. [bibtex-entry]


  42. S. Ronen and O. Shenkar. Clustering countries on attitudinal dimensions: A review and synthesis. Academy of Management Review, 10(3):435-454, 1985. Keyword(s): cluster analysis, correlation (statistical), econometrics, random variables, research, statistics, quantitative research. [bibtex-entry]


  43. T. Lane. A k-th nearest neighbour clustering procedure. Journal of the Royal Statistical Society. Series B (Methodological), 45(3):362-368, 1983. Keyword(s): clustering procedure, high density clusters, k-th nearest neighbour density estimation, set-consistency. [bibtex-entry]


  44. T. Lane. A k-th nearest neighbour clustering procedure. Journal of the Royal Statistical Society. Series B (Methodological), 45(3):362-368, 1983. Keyword(s): clustering procedure, high density clusters, k-th nearest neighbour density estimation, set-consistency. [bibtex-entry]


  45. M. Wong. A k-th nearest neighbour clustering procedure. Journal of the Royal Statistical Society. Series B (Methodological), 45(3):362-368, 1983. Keyword(s): clustering procedure, high density clusters, k-th nearest neighbour density estimation, set-consistency. [bibtex-entry]


  46. M. Wong. A k-th nearest neighbour clustering procedure. Journal of the Royal Statistical Society. Series B (Methodological), 45(3):362-368, 1983. Keyword(s): clustering procedure, high density clusters, k-th nearest neighbour density estimation, set-consistency. [bibtex-entry]


  47. W. Rand. Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66(336):846-850, 1971. [bibtex-entry]


  48. W. Williams. Principles of clustering. Annual Review of Ecology and Systematics, 2:303-326, 1971. [bibtex-entry]


Conference articles
  1. N. Belacel, M. Cuperlovic-Culf, and M. Boulassel. The variable neighborhood search metaheuristic for fuzzy clustering CDNA microarray gene expression data. In Proceedings of IASTED International Conference on Artificial Intelligence and Applications, Innsbruck, Austria, 2004. [bibtex-entry]


  2. Y. Guan, A. Ghorbani, and N. Belacel. An unsupervised clustering algorithm for intrusion detection: Advances in artificial intelligence. In 16th Conference of the Canadian Society for Computational Studies of Intelligence, Ontario, Canada, pages 616-117, 2003. Halifax, Springer-Verlag. [bibtex-entry]


  3. Y. Guan, A. Ghorbani, and N. Belacel. Y-means: A clustering method for intrusion detection. In Canadian Conference on Electrical and Computer Engineering, Montreal, Quebec, Canada, 2003. [bibtex-entry]


Internal reports
  1. M. Kumar and N. Patel. Clustering data with measurement errors. Technical report RRR 12-2005, RUTCOR, Rutgers Center for Operations Research, New Jersey, 2005. [bibtex-entry]


  2. J. Figueira, Y. De Smet, and J.-P. Brans. MCDA methods for sorting and clustering problems: PROMETHEE TRI and PROMETHEE CLUSTER. Technical report 12, INESC Coimbra, Coimbra, 2003. Keyword(s): multiple criteria decision analysis, sorting, clustering, PROMETHEE methodology. [bibtex-entry]



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Last modified: Fri Dec 15 07:05:47 2006
Author: Juscelino Dias.


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