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资源说明：machine learning library
#Machine Learning toolbox ##overview The mlplus source code repository is used as machine learning library. we provide a simple and fast basic machine learning algorithms, such as 1. navie bayes classifier (http://en.wikipedia.org/wiki/Naive_Bayes_classifier) 2. standard decision tree (c4.5 etc.) 3. The k-means algorithm 4. EM algorithm 5. The Apriori algorithm 6. adboost (http://en.wikipedia.org/wiki/AdaBoost) 7. gbdt(gradient boosting decision tree(http://en.wikipedia.org/wiki/Gradient_boosting), 8. svm e.t.c ## list of the machine learning ###Classification ================== #####1. C4.5 Quinlan, J. R. 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc. #####2. CART L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth, Belmont, CA, 1984. ####3. K Nearest Neighbours (kNN) Hastie, T. and Tibshirani, R. 1996. Discriminant Adaptive Nearest Neighbor Classification. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI). 18, 6 (Jun. 1996), 607-616. DOI= http://dx.doi.org/10.1109/34.506411 ####4. Naive Bayes Hand, D.J., Yu, K., 2001. Idiot's Bayes: Not So Stupid After All? Internat. Statist. Rev. 69, 385-398. ###Statistical Learning ==================== ####5. SVM Vapnik, V. N. 1995. The Nature of Statistical Learning Theory. Springer-Verlag New York, Inc. ####6. EM McLachlan, G. and Peel, D. (2000). Finite Mixture Models. J. Wiley, New York. ###Association Analysis ==================== ####7. Apriori Rakesh Agrawal and Ramakrishnan Srikant. Fast Algorithms for Mining Association Rules. In Proc. of the 20th Int'l Conference on Very Large Databases (VLDB '94), Santiago, Chile, September 1994. http://citeseer.comp.nus.edu.sg/agrawal94fast.html ####8. FP-Tree Han, J., Pei, J., and Yin, Y. 2000. Mining frequent patterns without candidate generation. In Proceedings of the 2000 ACM SIGMOD international Conference on Management of Data (Dallas, Texas, United States, May 15 - 18, 2000). SIGMOD '00. ACM Press, New York, NY, 1-12. DOI= http://doi.acm.org/10.1145/342009.335372 ###Link Mining =================== ####9. PageRank Brin, S. and Page, L. 1998. The anatomy of a large-scale hypertextual Web search engine. In Proceedings of the Seventh international Conference on World Wide Web (WWW-7) (Brisbane, Australia). P. H. Enslow and A. Ellis, Eds. Elsevier Science Publishers B. V., Amsterdam, The Netherlands, 107-117. DOI= http://dx.doi.org/10.1016/S0169-7552(98)00110-X ####10. HITS Kleinberg, J. M. 1998. Authoritative sources in a hyperlinked environment. In Proceedings of the Ninth Annual ACM-SIAM Symposium on Discrete Algorithms (San Francisco, California, United States, January 25 - 27, 1998). Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, Philadelphia, PA, 668-677. ###Clustering =============== ####11. K-Means MacQueen, J. B., Some methods for classification and analysis of multivariate observations, in Proc. 5th Berkeley Symp. Mathematical Statistics and Probability, 1967, pp. 281-297. ####12. BIRCH Zhang, T., Ramakrishnan, R., and Livny, M. 1996. BIRCH: an efficient data clustering method for very large databases. In Proceedings of the 1996 ACM SIGMOD international Conference on Management of Data (Montreal, Quebec, Canada, June 04 - 06, 1996). J. Widom, Ed. SIGMOD '96. ACM Press, New York, NY, 103-114. DOI= http://doi.acm.org/10.1145/233269.233324 ###Bagging and Boosting ==================== ####13. AdaBoost Freund, Y. and Schapire, R. E. 1997. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 1 (Aug. 1997), 119-139. DOI= http://dx.doi.org/10.1006/jcss.1997.1504 ###Sequential Patterns =================== ####14. GSP Srikant, R. and Agrawal, R. 1996. Mining Sequential Patterns: Generalizations and Performance Improvements. In Proceedings of the 5th international Conference on Extending Database Technology: Advances in Database Technology (March 25 - 29, 1996). P. M. Apers, M. Bouzeghoub, and G. Gardarin, Eds. Lecture Notes In Computer Science, vol. 1057. Springer-Verlag, London, 3-17. ####15. PrefixSpan J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal and M-C. Hsu. PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In Proceedings of the 17th international Conference on Data Engineering (April 02 - 06, 2001). ICDE '01. IEEE Computer Society, Washington, DC. ###Integrated Mining ====================== ####16. CBA Liu, B., Hsu, W. and Ma, Y. M. Integrating classification and association rule mining. KDD-98, 1998, pp. 80-86. http://citeseer.comp.nus.edu.sg/liu98integrating.html ###Rough Sets ====================== ####17. Finding reduct Zdzislaw Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Norwell, MA, 1992 ###Graph Mining ======================= ####18. gSpan Yan, X. and Han, J. 2002. gSpan: Graph-Based Substructure Pattern Mining. In Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM '02) (December 09 - 12, 2002). IEEE Computer Society, Washington, DC. * to get source: * to run it: * to get command line help run: * to install * in tools dir
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