A transaction-based neighbourhood-driven approach to quantifying interestingness of association rules

Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

Volumn , Issue , 2004, Pages 194-201

  Shekar, B ^a   Natarajan, Rajesh ^b  

^a INDIAN INSTITUTE OF MANAGEMENT BANGALORE   (India)

^b INDIAN INSTITUTE OF MANAGEMENT   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ASSOCIATION RULES (AR); CARDINALITY; ITEM RELATEDNESS; REAL LIFE DATASETS;

DATA PROCESSING; DATABASE SYSTEMS; DECISION SUPPORT SYSTEMS; INFORMATION RETRIEVAL; KNOWLEDGE BASED SYSTEMS; LEARNING SYSTEMS;

DATA MINING;

EID: 19544367252     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2004.10107     Document Type: Conference Paper

References (20)

1. A. A. Freitas. On Rule Interestingness Measures. Knowledge-Based Systems, 12, 1999, pp. 309-315.
2. A. Silberschatz, and A. Tuzhilin. What makes Patterns Interesting in Knowledge Discovery Systems. IEEE Transactions on Knowledge and Data Engineering, 8(6), 1996, pp. 970-974.
3. B. Liu, W. Hsu, L. Mun, and H. Lee. Finding Interesting Patterns Using User Expectations. IEEE Transactions on Knowledge and Data Engineering, 11(6), 1999, pp. 817-832.
4. B. Liu, W. Hsu, S. Chen, and Y. Ma. Analyzing the Subjective Interestingness of Association Rules. IEEE Intelligent Systems, 15(5), 2000, pp. 47-55.
5. B. Padmanabhan, and A. Tuzhilin. Unexpectedness as a Measure of Interestingness in Knowledge Discovery. Decision Support Systems, 27(3), 1999, pp. 303-318.
6. B. Shekar, and R. Natarajan. A Framework for Evaluating Knowledge-based Interestingness of Association Rules. Fuzzy Optimization and Decision Making, 3(2), June 2004, pp. 157-185.
7. E. R. Omiecinski. Alternative Interest Measures for Mining Associations in Databases. IEEE Transactions on Knowledge and Data Engineering, 15(1), Jan/Feb 2003, pp. 57-69.
8. G. Adomavicius, and A. Tuzhilin. Discovery of Actionable Patterns in Databases: The Action Hierarchy Approach. Proceedings of the Third International Conference on Knowledge Discovery and Data Mining(KDD -1997), AAAI, 1997, pp. 111-114.
9. G. Dong, and J. Li. Interestingness of Discovered Association Rules in Terms of Neighborhood-Based Unexpectedness. Proceedings of the Second Pacific-Asia Conference on Knowledge Discovery and Data Mining, 1998, pp.72-86.
10. J. F. Roddick, and S. Rice. What's Interesting About Cricket? - On Thresholds and Anticipation in Discovered Rules. SIGKDD Explorations, 3(1), 2001, pp. 1-5.
11. P. Tan, and V. Kumar. Interestingness Measures for Association Patterns: A Perspective. KDD'2000 Workshop on Postprocessing in Machine Learning and Data Mining, Boston, MA, August 2000.
12. P. Tan, V. Kumar, V. and J. Srivastava. Selecting the Right Interestingness Measure for Association Patterns. Information Systems, 29(4), June 2004, pp. 293-331.
13. R. J. Hilderman, and H. J. Hamilton. Knowledge Discovery and Interestingness Measures: A Survey, Technical Report, Department of Computer Science, University of Regina, Canada, 1999.
14. R. Meo. Theory of Dependence Values. ACM Transactions on Database Systems, 25(3), September 2000, pp. 380-406.
15. S. Brin, R. Motwani, and C. Silverstein. Beyond Market Baskets: Generalizing Association Rules to Correlations. Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, May 1997, pp.265-276.
16. S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. Dynamic Itemset Counting and Implication Rules for Market Basket Data. Proceedings of the ACM SIGMOD International Conference on Management of Data, May 1997, pp. 255-264.
17. th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD 2001), Springer-Verlag, LNAI 2168, Freiburg, September 2001, pp. 253-266.
18. S. Sahar. Interestingness Via What is Not Interesting. Proceedings of the 5th ACM, SIGKDD International Conference on Knowledge Discovery and Data Mining, 1999, pp. 332-336.
19. T. Brijs, G. Swinnen, K. Vanhoof, and G. Wets. Building and Association Rules Framework to Improve Product Assortment Decisions. Data Mining and knowledge Discovery, 8, 2004, pp. 7-23.
20. W. Teng, M. Hsieh, and M. Chen. On the Mining of Substitution Rules for Statistically Dependent Items. Proceedings of IEEE International Conference on Data Mining (ICDM'02), 2002, pp. 442-449.