|
CSc 80000
Section 5 |
Susan P. Imberman Ph.D.
Assistant Professor
Computer Science Department
Voice Mail: 718-982-3273
Department Office: 718-982-982-2850
Email: imberman at mail dot csi dot cuny dot edu
Home Page: www.cs.csi.cuny.edu/~imberman/
LECTURE
NOTES
Lecture 2 - Why do Statisticians "hate" us?!
Lecture 4 - Student Presentations Anyone who presents and wishes me to link to or display their presentation, let me
know.
Lecture 5 -
Incremental
Association Rules
Lecture 6 - Incremental Association Rules - UWEP/NUWEP
Lecture 7 - Student Presentations
FOR PRESENTATION II
Your second
presentation will be on an application of association rules. The application can deal with general association
rules or one of the more "special" types we have discussed this
semester such as incremental, temporal, constraints, quantitative, etc. Each person will have 10 minutes to present. You may use the KDD conference industrial
track, IEEE International conference for Data Mining (ICDM ),
or www.kdnuggets.com for possible topics. Any applications that you have worked on are
welcome too, as long as we don’t violate any proprietary contracts. Have fun J .
FOR THOSE
WHO LIKE TO PLAY
WEKA
http://www.cs.waikato.ac.nz/ml/weka/
Christian Borgelt's version of Apriori
Apriori:
R. Srikant, R. Agrawal: "Mining Generalized Association Rules", Proc. of the 21st Int'l
Conference on Very Large Databases,
R. Agrawal,
H. Mannila, R. Srikant, H. Toivonen and A. I. Verkamo:
"Fast Discovery of Association Rules", Advances in Knowledge
Discovery and Data Mining, Chapter 12, AAAI/MIT Press, 1995.
R. Agrawal,
R. Srikant: "Fast
Algorithms for Mining Association Rules", Proc. of the 20th Int'l Conference on Very Large
Databases,
R. Agrawal,
T. Imielinski, A. Swami: "Mining
Associations between Sets of Items in Massive Databases", Proc. of the ACM-SIGMOD
1993 Int'l Conference on Management of Data,
Improving Apriori:
FP-Growth - J. Han, J. Pei, and Y. Yin, `` Mining Frequent
Patterns without Candidate Generation (PDF)'',
(Slides), Proc. 2
Dynamic
Hashing and Pruning (DHP) Jong Soo Park, Ming-Syan Chen, and
Philip S. Yu, "An
effective hash-based algorithm for mining association rules," Proceedings of the 1995 ACM SIGMOD, pp.
175-186, San Jose, May 1995. 000 ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD'00), allas, TX, May 2000.
Sampling Sampling large databases for association rules , Hannu
Toivonen. In 22th
International Conference on Very Large Databases (VLDB'96), 134 - 145,
Partition
Ashok Savasere,
Edward Omiecinski, and Shamkant
Navathe. An efficient algorithm for mining
association rules in large databases. In Proceedings of the 21st VLDB Conference, pages
432-443,
Incremental Association
Rule Algorithms:
FUP
- David W. Cheung, J. Han, V. Ng, and C.Y. Wong,
Maintenance of
Discovered Association Rules in Large Databases: An Incremental Updating
Techniques. Proc.
12th IEEE International Conference on Data Engineering (ICDE-96), New
FUP2 - David
W. Cheung, S.D. Lee, B. Kao, A General Incremental
Technique for Updating Discovered Association Rules. Proc. International
Conference On Database Systems For Advanced Applications (DASFAA-97),
UWEP - Necip Fazil Ayan, Abdullah Uz Tansel, and Erol Arkun. "An Efficient Algorithm to Update Large Itemsets with Early
Pruning".
ACM SIGKDD Intl. Conf. on Knowledge Discovery in
Data and Data Mining(SIGKDD'99),
Negative Borders - Shiby Thomas, Sreenath Bodagala, Khaled Alsabti, Sanjay Ranka. An Efficient Algorithm for the Incremental Updation of Association Rules in Large Databases. In
Proceedings of the 3rd International conference on Knowledge Discovery and Data
Mining (KDD 97),
NUWEP - An
Efficient Method For Finding Emerging Large Itemsets, Susan P. Imberman,
Abdullah Uz Tansel, Eric Pacuit,
The Third Workshop on Mining Temporal and Sequential Data, ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining, August 2004
Sliding Window Filtering - C.-H.
Lee, C.-R. Lin and M.-S. Chen, ``Sliding-Window
Filtering: An Efficient Algorithm for Incremental Mining,'' Proc. of the ACM 10th International Conference on
Information and Knowledge Management (CIKM-01),
More ….