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Quinlan, J. R. 1993. C4.5: Programs for Machine Learning.
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Hastie, T. and Tibshirani, R. 1996. Discriminant Adaptive Nearest
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Hand, D.J., Yu, K., 2001. Idiot's Bayes: Not So Stupid After All?
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Statistical Learning
====================
#5. SVM
Vapnik, V. N. 1995. The Nature of Statistical Learning
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McLachlan, G. and Peel, D. (2000). Finite Mixture Models.
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Association Analysis
====================
#7. Apriori
Rakesh Agrawal and Ramakrishnan Srikant. Fast Algorithms for Mining
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Han, J., Pei, J., and Yin, Y. 2000. Mining frequent patterns without
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Link Mining
===========
#9. PageRank
Brin, S. and Page, L. 1998. The anatomy of a large-scale hypertextual
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#10. HITS
Kleinberg, J. M. 1998. Authoritative sources in a hyperlinked
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Clustering
==========
#11. K-Means
MacQueen, J. B., Some methods for classification and analysis of
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Zhang, T., Ramakrishnan, R., and Livny, M. 1996. BIRCH: an efficient
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Bagging and Boosting
====================
#13. AdaBoost
Freund, Y. and Schapire, R. E. 1997. A decision-theoretic
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Sequential Patterns
===================
#14. GSP
Srikant, R. and Agrawal, R. 1996. Mining Sequential Patterns:
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J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal and
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Integrated Mining
=================
#16. CBA
Liu, B., Hsu, W. and Ma, Y. M. Integrating classification and
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Rough Sets
==========
#17. Finding reduct
Zdzislaw Pawlak, Rough Sets: Theoretical Aspects of Reasoning about
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Graph Mining
============
#18. gSpan
Yan, X. and Han, J. 2002. gSpan: Graph-Based Substructure Pattern
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