Schedule for 2009 (Data Mining (Spring 2009))

Week	Date	Topic	Content	Student	Remarks
1	Feb. 24, 2009	Preliminary	Chapter 1 (Introduction)
2	Mar. 3, 2009	Preliminary	Chapter 2 (Data)
3	Mar. 10, 2009	Preliminary	Chapter 3 (Exploring Data)		Assignment #1 Out
4	Mar. 17, 2009	Predictive Data Mining	Chapter 4.(Classification) 4.3 Decision Tree Induction 4.4 Model Overfitting	陳灝儒	KDD Cup 2009
5	Mar. 24, 2009	Predictive Data Mining	4.5~4.6 Model Evaluation 5.1 Rule-Based Classifier	黃致凱	Assignment #1 Due
6	Mar. 31, 2009	Predictive Data Mining	5.3 Bayesian Classifiers 5.4 Artificial Neural Network	康惟翔	Assignment #2 Out
7	Apr. 7, 2009	Predictive Data Mining	5.5 Support Vector Machine 5.6 Ensemble Methods 5.7 Class Imbalance Problem	余幸娟黃安慶
8	Apr. 14, 2009	Cost-Sensitive Learning Sample Selection Bias	See below	劉文港林逸農江桄紘	Assignment #2 Due
9	Apr. 21, 2009	Association Analysis
10	Apr. 28, 2009	Association Analysis	6.5~6.6 FP-Growth	林衍伶
11	May 5, 2009	Association Analysis	7.4~7.5 Sequential pattern	徐誌良
12	May 12, 2009	Sequence Labeling	Hidden Markov Models	江一杰
13	May 19, 2009	Sequence Labeling	Conditional Random Fields	廖長彥吳睦傑張安天
14	May 26, 2009	Project Demo	Project Demonstration	All
15	Jun. 2, 2009	Cluster Analysis	2.4 Measures of similarity and dissimilarity	官直毅
16	Jun. 9, 2009	Cluster Analysis
17	Jun.16, 2009	Cluster Analysis
18	Jun. 23, 2009	Final Exam

Cost-sensitive learning & Sample selection bias correction:

Zadrozny, B., Langford, J. and Abe, N. Cost-Sensitive learning by cost-proportionate example weighting. Proceedings of the Third IEEE International Conference on Data Mining. 2003
Zadrozny, B. Learning and evaluating classifiers under sample selection bias. In Proceedings of the 21th International Conference on Machine Learning, 2004
Smith, A. and Elkan, C. (2004). A bayesian network framework for reject inference. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 286–295.
Fan, W. and I. Davidson, On Sample Selection Bias and Its Efficient Correction via Model Averaging and Unlabeled Examples. In Proceedings of the SIAM International Conference on Data Mining, SDM 2007.

Sequence labeling:

Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proc. 18th International Conf. on Machine Learning, Morgan Kaufmann, San Francisco, CA (2001) 282–289 (http://www.cis.upenn.edu/~pereira/papers/crf.pdf)
Sutton, C., McCallum, A.: An Introduction to Conditional Random Fields for Relational Learning. In "Introduction to Statistical Relational Learning". Edited by Lise Getoor and Ben Taskar. MIT Press. (2006) Online PDF
Klinger, R., Tomanek, K.: Classical Probabilistic Models and Conditional Random Fields. Algorithm Engineering Report TR07-2-013, Department of Computer Science, Dortmund University of Technology, December 2007. ISSN 1864-4503. Online PDF

DM5.3 Bayesian Classifier.ppt - on May 18, 2009 10:33 PM by Jahui chang (version 4 / earlier versions)
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DM5.6 ensemble method.pdf - on May 18, 2009 10:42 PM by Jahui chang (version 1)
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DM5.7 class imbalance.ppt - on May 18, 2009 10:36 PM by Jahui chang (version 1)
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Learning and Evaluating Classiﬁers under Sample Selection Bias.ppt - on May 18, 2009 10:42 PM by Jahui chang (version 1)
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klinger_crf.ppt - on May 18, 2009 10:31 PM by Jahui chang (version 1)
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lafferty_crf_廖長彥.pdf - on May 18, 2009 10:31 PM by Jahui chang (version 1)
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sutton_crf.ppt - on May 18, 2009 10:31 PM by Jahui chang (version 1)
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Data Mining (Spring 2009)