22C:242 Presentation Schedule

Date Presenter Commenters Reviewers Topic Papers

Part 1: Association Rules

2/27 Li Littke, Mejova none FP trees J. Han, J. Pei, and Y. Yin, Mining frequent patterns without candidate generation, Proc. 2000 ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD'00), Dallas, TX, May 2000.

3/3 Shukla Duan, Engler Chen, Chopra, Das, DeLuca, Thole Correlation mining C. Jermaine, Finding the most interesting correlations in a database: How hard can it be?, Information Systems 30(1): 21-46, March 2005.

3/5 Duan Das,Khoshneshin Hylock, Li, Littke, Mejova, Shukla Statistical significance in ARs G. I. Webb, Discovering significant patterns, Machine Learning 68(1):1-34, July 2007.

Part 2: Classification

3/10 Khoshneshin Hylock, Li Suryakumar, Thole, Wang, Yasar Saglam, Chen Collaborative filtering L. Candillier, F. Meyer and M. Coulle, Comparing state-of-the-art collaborative filtering systmes, in Machine Learning and Data Mining in Pattern Recognition, pages 548-562, Springer Lecture Notes in Computer Science, 2007.

3/12 Das Shukla, Chen Duan, Chopra, DeLuca, Engler, Li Random forests L. Breiman, Random forests, Machine Learning 45(1):5-32, 2001

3/24 Chopra Engler, Yasar Saglam Das, Duan, Hylock, Khoshneshin, Littke No-free-lunch theorems C. Schaffer, Overfitting avoidance as bias, Machine Learning 10:153-178, 1993.

3/26 Mejova Dong, Littke Shukla, Suryakumar, Thole, Engler, Yasar Saglam Reinforcement learning L. Pack Kaelbling, M. L. Littman and A. W. Moore, Reinforcement learning: A survey, Journal of Aritificial Intelligence Research 4:237-285, 1996. (focus on TD-learning and Q-learning)

3/31 Littke Suryakumar, Chopra Khoshneshin, Mejova, Chen, Das, Duan Privacy-preserving data mining R. Agrawal and R. Srikant, Privacy-preserving data mining, Proc. of the ACM SIGMOD Conference on Management of Data, Dallas, TX, May 2000, pages 439-450.

4/2 DeLuca Mejova, Dong Chopra, Thole, Hylock, Li, Littke Semisupervised learning K.P. Bennett and A. Demiriz, Semi-supervised support vector machines, in Advances in Neural Information Processing Systems 12, MIT Press 1998, pages 368-374.

Part 3: Clustering

4/7 Engler Wang, Duan DeLuca, Khoshneshin, Mejova, Shukla, Suryakumar Density-based clustering J. Sander, M. Ester, H.-P. Kriegel, X. Xu, Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications, Data Mining and Knowledge Discovery 2(2):169-194, June 1998

4/9 Dong Thole, Shukla Wang, Yasar Saglam, Chen, Chopra, Das Clustering for large datasets D. M. Rocke and J. Dai, Sampling and subsampling for cluster analysis in data mining: With applications to sky survey data, Data Mining and Knowledge Discovery 7(2):215-232, April 2003.

4/14 Chen DeLuca, Li Thole, Duan, Hylock, Khoshneshin, Wang Subspace clustering R. Agrawal, J. Gehrke, D. Gunopulos and P. Raghavan, Automatic subspace clustering of high dimensional data, Data Mining and Knowledge Discovery 11(1):5-34, July 2005.

4/16 Yasar Saglam Suryakumar, Khoshneshin DeLuca, Li, Littke, Mejova, Suryakumar Ensemble clustering A. Strehl and J. Ghosh, Cluster ensembles: A knowledge reuse framework for combining multiple partitions, Journal of Machine Learning Research 3:583-617, December 2002.

Part 4: Assorted Topics

4/21 Wang Hylock, Das Yasar Saglam, Chen, Chopra, Engler, Shukla Temporal mining M. Vlachos, K.-L. Wu, S.-K. Chen, P. S. Yu, Correlating burst events on streaming stock market data, Data Mining and Knowledge Discovery 16(1):109-133, February 2008.

4/23 Suryakumar Thole, Yasar Saglam Das, DeLuca, Duan, Engler, Hylock Mining streaming data W. N. Street and Y. Kim. A streaming ensemble algorithm (SEA) for large-scale classification. Seventh ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 377-382, San Francisco, CA, August 2001.

4/28 Hylock Wang, Chen Wang, Khoshneshin, Li, Littke, Mejova Relational mining C. Perlich and F. Provost, Distribution-based aggregation for relational learning with identifier attributes, Machine Learning 62(1/2):65-106, February 2006.

4/30 Thole DeLuca, Chopra Engler, Shukla, Suryakumar, Wang, Yasar Saglam Multimedia mining K. Selçuk Candan, J. W. Kim, H. Liu, R. Suvarna and N. Agarwal, Exploiting spatial transformations for identifying mappings in hierarchical media data, In Multimedia Data Mining and Knowledge Discovery, Springer, 2007.

Date	Presenter	Commenters	Reviewers	Topic	Papers
Part 1: Association Rules
2/27	Li	Littke, Mejova	none	FP trees	J. Han, J. Pei, and Y. Yin, Mining frequent patterns without candidate generation, Proc. 2000 ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD'00), Dallas, TX, May 2000.
3/3	Shukla	Duan, Engler	Chen, Chopra, Das, DeLuca, Thole	Correlation mining	C. Jermaine, Finding the most interesting correlations in a database: How hard can it be?, Information Systems 30(1): 21-46, March 2005.
3/5	Duan	Das,Khoshneshin	Hylock, Li, Littke, Mejova, Shukla	Statistical significance in ARs	G. I. Webb, Discovering significant patterns, Machine Learning 68(1):1-34, July 2007.
Part 2: Classification
3/10	Khoshneshin	Hylock, Li	Suryakumar, Thole, Wang, Yasar Saglam, Chen	Collaborative filtering	L. Candillier, F. Meyer and M. Coulle, Comparing state-of-the-art collaborative filtering systmes, in Machine Learning and Data Mining in Pattern Recognition, pages 548-562, Springer Lecture Notes in Computer Science, 2007.
3/12	Das	Shukla, Chen	Duan, Chopra, DeLuca, Engler, Li	Random forests	L. Breiman, Random forests, Machine Learning 45(1):5-32, 2001
3/24	Chopra	Engler, Yasar Saglam	Das, Duan, Hylock, Khoshneshin, Littke	No-free-lunch theorems	C. Schaffer, Overfitting avoidance as bias, Machine Learning 10:153-178, 1993.
3/26	Mejova	Dong, Littke	Shukla, Suryakumar, Thole, Engler, Yasar Saglam	Reinforcement learning	L. Pack Kaelbling, M. L. Littman and A. W. Moore, Reinforcement learning: A survey, Journal of Aritificial Intelligence Research 4:237-285, 1996. (focus on TD-learning and Q-learning)
3/31	Littke	Suryakumar, Chopra	Khoshneshin, Mejova, Chen, Das, Duan	Privacy-preserving data mining	R. Agrawal and R. Srikant, Privacy-preserving data mining, Proc. of the ACM SIGMOD Conference on Management of Data, Dallas, TX, May 2000, pages 439-450.
4/2	DeLuca	Mejova, Dong	Chopra, Thole, Hylock, Li, Littke	Semisupervised learning	K.P. Bennett and A. Demiriz, Semi-supervised support vector machines, in Advances in Neural Information Processing Systems 12, MIT Press 1998, pages 368-374.
Part 3: Clustering
4/7	Engler	Wang, Duan	DeLuca, Khoshneshin, Mejova, Shukla, Suryakumar	Density-based clustering	J. Sander, M. Ester, H.-P. Kriegel, X. Xu, Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications, Data Mining and Knowledge Discovery 2(2):169-194, June 1998
4/9	Dong	Thole, Shukla	Wang, Yasar Saglam, Chen, Chopra, Das	Clustering for large datasets	D. M. Rocke and J. Dai, Sampling and subsampling for cluster analysis in data mining: With applications to sky survey data, Data Mining and Knowledge Discovery 7(2):215-232, April 2003.
4/14	Chen	DeLuca, Li	Thole, Duan, Hylock, Khoshneshin, Wang	Subspace clustering	R. Agrawal, J. Gehrke, D. Gunopulos and P. Raghavan, Automatic subspace clustering of high dimensional data, Data Mining and Knowledge Discovery 11(1):5-34, July 2005.
4/16	Yasar Saglam	Suryakumar, Khoshneshin	DeLuca, Li, Littke, Mejova, Suryakumar	Ensemble clustering	A. Strehl and J. Ghosh, Cluster ensembles: A knowledge reuse framework for combining multiple partitions, Journal of Machine Learning Research 3:583-617, December 2002.
Part 4: Assorted Topics
4/21	Wang	Hylock, Das	Yasar Saglam, Chen, Chopra, Engler, Shukla	Temporal mining	M. Vlachos, K.-L. Wu, S.-K. Chen, P. S. Yu, Correlating burst events on streaming stock market data, Data Mining and Knowledge Discovery 16(1):109-133, February 2008.
4/23	Suryakumar	Thole, Yasar Saglam	Das, DeLuca, Duan, Engler, Hylock	Mining streaming data	W. N. Street and Y. Kim. A streaming ensemble algorithm (SEA) for large-scale classification. Seventh ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 377-382, San Francisco, CA, August 2001.
4/28	Hylock	Wang, Chen	Wang, Khoshneshin, Li, Littke, Mejova	Relational mining	C. Perlich and F. Provost, Distribution-based aggregation for relational learning with identifier attributes, Machine Learning 62(1/2):65-106, February 2006.
4/30	Thole	DeLuca, Chopra	Engler, Shukla, Suryakumar, Wang, Yasar Saglam	Multimedia mining	K. Selçuk Candan, J. W. Kim, H. Liu, R. Suvarna and N. Agarwal, Exploiting spatial transformations for identifying mappings in hierarchical media data, In Multimedia Data Mining and Knowledge Discovery, Springer, 2007.