CS 6604 Lectures

• Introduction to CS 6604
  
  Aug 20: A scribing schedule was agreed upon for future lectures.
  Use this template (meant to be PDFLaTeXed; courtesy Erik Demaine) to prepare your notes.
  
  
• Itemsets and Rules
  
  
  Aug 22: Frequent Itemset Mining (files)
  
  Aug 27: Mining Closed and Maximal Sets
  
  Aug 29: Mining Rules
  
  Sep 3: Pattern Growth Algorithms
  
  Sep 5: Support Bounding Techniques
  
  Sep 10: Pushing constraints into Data Mining
  
  Sep 12: More Constraints
  
  
• Classification
  
  
  Sep 17: Decision Trees
  
  Sep 19: More on Classification
  
  
• Relational Data Mining
  
  
  Sep 24: Introduction
  
  Sep 26: Inverse Resolution
  
  Oct 1: Theta-subsumption
  
  Oct 3: Introduction to PROGOL
  
  Oct 10: More PROGOL
  
  Oct 15: Relational Association Rules
  
  
• Probabilistic Methods
  
  
  Oct 17: Basics
  
  Oct 22: ML and MAP Hypotheses
  
  Oct 24: Least Squares, NBC, and other paradigms
  
  

References

Itemsets and Rules

1. R. Agrawal, T. Imielinksi, and A. Swami, Mining Association Rules between Sets of Items in Large Databases, in Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD'93) , pages 207-216, 1993.
   
   
2. R. Agrawal and R. Srikant, Fast Algorithms for Mining Association Rules, in Proceedings of the 20th International Conference on Very Large Data Bases (VLDB'94), pages 487-499, 1994.
   
   
3. R. Bayardo Jr., Efficiently Mining Long Patterns from Databases, in Proc. of the 1998 ACM-SIGMOD Int'l Conf. on Management of Data, pages 85-93, 1998.
   
   
4. F. Bonchi, F. Giannotti, A. Mazzanti, and D. Pedreschi, ExAnte: Anticipated Data Reduction in Constrained Pattern Mining, in Proceedings of PKDD'03, 2003.
   
   
5. F. Bonchi, C. Lucchese, and R. Trasarti, Pushing Tougher Constraints in Frequent Pattern Mining, in Proceedings of PAKDD'05, pages 114-124, 2005.
   
   
6. S. Brin, R. Motwani, and C. Silverstein, Beyond Market Baskets: Generalizing Association Rules to Correlations, in Proc. ACM SIGMOD'97, pages 265-276, 1997.
   
   
7. D. Burdick, M. Calimlim, and J. Gehrke, MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases, In Proceedings of the 17th International Conference on Data Engineering, Heidelberg, Germany, April 2001.
   
   
8. T. Calders and B. Goethals, Mining all Non-Derivable Itemsets, Data Mining and Knowledge Discovery, 2006.
   
   
9. [Review] A. Ceglar and J.F. Roddick, Association Mining, ACM Computing Surveys, Vol. 38, No. 2, Article No. 5, July 2006.
   
   
10. J. Han, J. Pei, Y. Yin, and R. Mao, Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach, Data Mining and Knowledge Discovery, Volume 8, Issue 1, pages 53-87, January 2004,
    
    
11. D.-I. Lin and Z.M. Kedem, Pincer Search: A New Algorithm for Discovering the Maximal Frequent Set, in Advances in Database Technology: 6th International Conference on Extending Database Technology (EDBT'98), pages 105-119, 1998.
    
    
12. H. Mannila and H. Toivonen, Multiple Uses of Frequent Sets and Condensed Representations, in Proc. KDD'96, 1996.
    
    
13. H. Mannila, H. Toivonen, and A.I. Verkamo, Efficient Algorithms for Discovering Association Rules, in Proceedings of the AAAI Workshop on Knowledge Discovery in Databases (KDD), pages 181-192, 1994.
    
    
14. N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, Discovering Frequent Closed Itemsets for Association Rules, in Proceedings of ICDT'99, pages 398-416, 1999.
    
    
15. J. Pei and J. Han, Can we push more Constraints into Frequent Pattern Mining? in Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 350-354, 2000.
    
    
16. M. Zaki, Generating Non-Redundant Association Rules, in Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 34-43, 2000.
    
    
17. M. Zaki and K. Gouda, Fast Vertical Mining using Diffsets, in Proceedings of KDD'03, 2003.
    
    
18. M. Zaki and C.-J. Hsiao, CHARM: An Efficient Algorithm for Closed Itemset Mining, in Proceedings of the Second SIAM International Conference on Data Mining, 2002.
    
    
19. M. Zaki and M. Ogihara, Theoretical Foundations of Association Rules, in Proceedings of the SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD'98), 1998.
    
    
20. M. Zaki, S. Parthasarathy, M. Ogihara, and W. Li, New Algorithms for Fast Discovery of Association Rules, in Proceedings 3rd International Conference on Knowledge Discovery and Data Mining (KDD'97), pages 283-286, Aug 1997.
    
    
21. M. Zaki and N. Ramakrishnan, Reasoning about Sets using Redescription Mining, in Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'2005), Chicago, IL, pages 364-373, Aug 2005.
    

Classification

1. J. Gehrke, V. Ganti, R. Ramakrishnan, and W.-Y. Loh, BOAT: Optimistic Decision Tree Construction, in Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD'99), 1999.
   
   
2. Decision Tree Tutorial by Andrew Moore
   
   
3. Information Gain Tutorial by Andrew Moore

Relational Data Mining

1. L. Dehaspe and H. Toivonen, Discovery of Relational Association Rules, in N. Lavrac and S. Dzeroski, Relational Data Mining, Springer-Verlag, 2000.
   
   
2. S. Muggleton and J. Firth, CProgol 4.4: A Tutorial Introduction, in N. Lavrac and S. Dzeroski, Relational Data Mining, Springer-Verlag, 2000. Available at: http://citeseer.ist.psu.edu/309049.html.
   

Probabilistic Methods

1. Probability for Data Miners Tutorial by Andrew Moore
   
   
2. Probability Density Functions Tutorial by Andrew Moore
   
   
3. Naive Bayes Tutorial by Andrew Moore