1. N. Belacel, H. Raval, and A. Punnen. Learning multicriteria fuzzy classification method PROAFTN from data. Computers & Operations Research, 34(7):1885-1898, 2007. Keyword(s): data mining, multiple criteria classification, PROAFTN, variable neighborhood search. [bibtex-entry]



2. 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]



3. H. Nakayama, Y. Yun, T. Asada, and M. Yoon. MOP/GP models for machine learning. European Journal of Operational Research, 166(3):756-768, 2005. Keyword(s): data mining, machine learning, linear classifier with maximal margin, support vector machine, goal programming. [bibtex-entry]



4. 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]



5. P. Hammer, A. Kogan, B. Simeone, and S. Szedmák. Pareto-optimal patterns in logical analysis of data. Discrete Applied Mathematics, 144(1-2):79-102, 2004. Keyword(s): extremal patterns, data mining, machine learning, classification accuracy, boolean functions. [bibtex-entry]



6. H. Ishibuchi and T. Yamamoto. Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets and Systems, 141(1):59-88, 2004. Keyword(s): data mining, pattern classification, fuzzy rule selection, evolutionary multi-criterion optimization, hybrid genetic algorithms. [bibtex-entry]



7. S. Sohn and C. Dagli. Ensemble of Evolving Neural Networks in Classification. Neural Processing Letters, 19(3):191-203, 2004. Keyword(s): classification, combining classifiers, data mining, genetic algorithm, neural networks. [bibtex-entry]



8. V. Ananthanarayana, M. Murty, and D. Subramanian. Tree structure for efficient data mining using rough sets. Pattern Recognition Letters, 24(6):851-862, 2003. Note: Short Communication. Keyword(s): PC-tree, single database scan, dynamic mining, segment PC-tree, rough PC-tree, classification, rough sets. [bibtex-entry]



9. Y.-C. Hu, R.-S. Chen, and G.-H. Tzeng. Finding fuzzy classification rules using data mining techniques. Pattern Recognition Letters, 24:509-519, 2003. Keyword(s): data mining, fuzzy sets, classification, genetic algorithm. [bibtex-entry]



10. 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]



11. I. Becerra-Fernandez, S. Zanakis, and S. Walczak. Knowledge discovery techniques for predicting country investment risk. Computers & Industrial Engineering, 43(4):787-800, 2002. Keyword(s): data mining, knowledge discovery, country investing risk. [bibtex-entry]



12. Y.-C. Hu, R.-S. Chen, and G.-H. Tzeng. Mining fuzzy association rules for classification problems. Computers & Industrial Engineering, 43:735-750, 2002. Keyword(s): data mining, knowledge acquisition, classification, association rules. [bibtex-entry]



13. S. Sánchez, E. Triantaphyllou, and D. Kraft. A feature mining based approach for the classfication of text documents into disjoint classes. Information Processing and Management, 38:583-604, 2002. Keyword(s): document classification, document indexing, vector space model, data mining, one clause at a time (OCAT) algorithm, machine learning. [bibtex-entry]



14. 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]



15. N. Ye and X. Li. A scalable, incremental learning algorithm for classification problems. Computers & Industrial Engineering, 43:677-692, 2002. Keyword(s): data mining, classification, incremental learning, scalability. [bibtex-entry]



16. V. Ananthanarayana, M. Murty, and D. Subramanian. An incremental data mining algorithm for compact realization of prototypes. Pattern Recognition, 34(11):2249-2251, 2001. Note: Short Communication. [bibtex-entry]



17. M. Beynon and M. Peel. Variable precision rough set theory and data discretisation: An application to corporate failure prediction. Omega - The International Journal of Management Science, 29:561-576, 2001. Keyword(s): data mining, failure prediction, FUSINTER data discretisation, rough set theory, variable precision rough set theory. [bibtex-entry]



18. T.-P. Hong, T.-T. Wang, S.-L. Wang, and B.-C. Chien. Learning a coverage set of maximally general fuzzy rules by rough sets. Expert Systems with Applications: An International Journal, 19:97-103, 2000. Keyword(s): machine learning, fuzzy sets, rough sets, data mining, expert system. [bibtex-entry]



19. H. Moshkovich, A. Mechitov, and R. Schellenberger. Effectiveness of ordinal information for data mining. IEEE - The Institute of Electrical and Electronics Engineers, pp 1882-1887, 2000. [bibtex-entry]



20. E. Boros, T. Ibaraki, and K. Makino. Logical analysis of binary data with missing bits. Artificial Intelligence, 107(2):219-263, 1999. Keyword(s): knowledge discovery, data mining, logical analysis of data, boolean functions, partial defined boolean functions, missing bits, NP-hardness. [bibtex-entry]



21. C. Emmanouilidis, A. Hunter, J. MacIntyre, and C. Cox. Multiple-criteria genetic algorithms for feature selection in neuro-fuzzy modeling. IEEE Transactions on Neural Networks, 6:4387-4392, 1999. Keyword(s): multiobjective genetic algorithms, fuzzy models, classification, feature selection, data mining. [bibtex-entry]



22. C.-C. Chan. A rough set approach to attribute generalization in data mining. Information Sciences: An International Journal, 107:169-176, 1998. Keyword(s): rough sets, data mining, inductive learning. [bibtex-entry]



23. S. Tsumoto. Extraction of experts' decision rules from clinical databases using rough set model. Intelligent Data Analysis, 2:215-227, 1998. Keyword(s): rough sets, data mining, knowledge discovery in databases, medical knowledge acquisition. [bibtex-entry]

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