Association mining in time-varying domains

Intelligent Data Analysis

Volumn 9, Issue 3, 2005, Pages 273-288

  Rozsypal, Antonin ^a   Kubat, Miroslav ^b  

^a UNIVERSITY OF LOUISIANA AT LAFAYETTE   (United States)

^b University of Miami College of Engineering   (United States)

Author keywords

association mining; change detection; similarity metrics

Indexed keywords

ASSOCIATION MINING; CHANGE DETECTION; ITEM SETS; PERFORMANCE CRITERION; SHOPPING CARTS; SIMILARITY METRICS; TIME-VARYING DOMAINS; TIME-VARYING ENVIRONMENTS;

EID: 67749084610     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/ida-2005-9304     Document Type: Conference Paper

References (46)

1. C.C. Aggarwal, C. Procopius and P.S. Yu, Finding Localized Associations in Market Basket Data, IEEE Transactions on Knowledge and Data Engineering 14 (2002), 51-62.
2. R. Agrawal, T. Imielinski and A. Swami, Mining Association Rules between Sets of Items in Large Databases, Proceedings of ACM SIGMOD (1993), 207-216.
3. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen and A.I. Verkamo, Fast Discovery of Association Rules, in: Advances in Knowledge Discovery and Data Mining, Fayyad et al., 1996, pp. 307-328.
4. R. Agrawal and J.C. Shafer, Parallel Mining of Association Rules, IEEE Transactions on Knowledge and Data Engineering 8 (December 1996), 962-969.
5. R. Agrawal and R. Srikant, Fast Algorithms for Mining Association Rules in Large Databases, Proceedings of the 20th International Conference on Very Large Databases, 1994, 478-499.
6. C.C. Aggarwal and P.S. Yu, A New Approach to Online Generation of Association Rules, IEEE Transactions on Knowledge and Data Engineering 13 (2001), 527-540.
7. Y. Aumann and Y. Lindell, A Statistical Theory for Quantitative Association Rules, Proceedings of the 5th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining (261-270), San Diego, 15-18, August, 1993.
8. R.J. Bayardo and R. Agrawal, Mining the Most Interesting Rules, Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, California, 1999, pp. 145-154.
9. P. Bollmann-Sdorra, A. Hafez and V.V. Raghavan, A Theoretical Framework for Association Mining based on the Boolean Retrieval Model, Data Warehousing and Knowledge Discovery: 3rd International Conference, (DaWaK-01), Y. Kambayashi, W. Winiwarter and M. Arikawa, eds, Munich, Germany, Sept., Springer, 2001, 21-30.
10. S. Brin, R. Motwani, J.D. Ullman and S. Tsur, Dynamic Itemset Counting and Implication Rules for Market Basket Data, SIGMOD Record 26(2) (1997), 255.
11. D.W. Cheung and J. Han, Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique, Proceedings of the 12th International Conference on Data Engineering, New Orleans, Louisiana, 1996.
12. D.W. Cheung, J. Han, V. Ng and C.Y. Wong, Maintenance of Discovered Association Rules in Large Databases, Proceedings of the International Conference on Data Engineering, New Orleans, LA, 1996.
13. D.W. Cheung, V.T. Ng, A.W. Fu and W. Fu, Efficient Mining of Association Rules in Distributed Databases, IEEE Transactions on Knowledge and Data Engineering 8 (1996), 911-922.
14. M.-S. Chen, J.S. Park and P.S. Yu, Data Mining for Path Traversal Patterns in Web Environments, Proceedings of the 16th International Conference on Distributed Computing Systems, 1996, 385-392.
15. V. Ganti, J. Gehrke and R. Ramakrishnan, Demon: Mining and Monitoring Evolving Data, Proceedings of the 16th International Conference on Data Engineering, Philadelphia, Pennsylvania, 1999.
16. J. Gehrke, V. Ganti and R. Ramakrishnan, Detecting Change in Categorical Data: Mining Contrast Sets, Proceedings of the 18th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, 2000, 126-137.
17. J. Han and Y. Fu, Mining Multiple-Level Association Rules in Large Databases, IEEE Transactions on Knowledge Engineering 11 (1999), 798-805.
18. M.B. Harries, Batch Learning in Domains with Hidden Changes in Context, PhD dissertation, School of Computer Science and Engineering, University of New South Wales, Australia, 1999.
19. M.B. Harries, C. Sammut and K. Horn, Extracting Hidden Context, Machine Learning 32 (1998), 101-126.
20. A.K. Jain and R.C. Dubes, Algorithms for Clustering Data, Englewood Cliffs, NJ, Prentice Hall, 1998.
21. J.D. Jobson, Applied Multivariate Data Analysis, (Vol. II), Categorical and Multivariate Methods, Springer Verlag.
22. M. Kubat, Floating Approximation in Time-Varying Knowledge Bases, Pattern Recognition Letters 10 (1989), 223-227.
23. M. Kubat, A. Hafez, V.V. Raghavan, J. Lekkala and W.K. Chen, Itemset Trees for On-Line Association Mining, IEEE Transactions on Knowledge and Data Engineering 15 (2003), 1522-1534.
24. M. Kubat and G.Widmer, Adapting to drift in continuous domains, Proceedings of the European Conference on Machine Learning ECML-95, Heraklion, Crete, 1995, 307-310.
25. B. Liu,W. Hsu and Y. Ma, Pruning and Summarizing the Discovered Associations, Proceedings of the 5th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining San Diego, California, 1999, 125-134.
26. M. Maloof, Progressive Partial Memory Learning, PhD Disertation, School of Information Technology and Engineering, George Mason University, Fairfax, Virginia, 1996.
27. M. Maloof and R.S. Michalski, Selecting Examples for Partial Memory Learning, Machine Learning 41 (2000), 27-52.
28. S. Matwin and M. Kubat, The Role of Context in Concept Learning, Workshop Notes of the Workshop on Learning in Context-Sensitive Domains, 1997, 1-5, Bari, Italy, July 3.
29. B. Nag, P.M. Deshpande and D. DeWitt, Using a Knowledge Cache for Interactive Discovery of Association Rules, Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1999, 244-253, San Diego, California.
30. S. Noel,V.V. Raghavan and C.H. Chu, Visualizing Association Mining Results through Hierarchical Clusters, Proceedings of the 2001 International Conf. on Data Mining (ICDM-01), San Jose, CA, Nov.-Dec, 2001, 425-432.
31. J.-S. Park, M.S. Chen and P.S. Yu, An Effective Hash Based Algorithm for Mining Association Rules, Proceedings of the ACM SIGMOD (1995), 175-186.
32. J.-S. Park, M.S. Chen and P.S. Yu, Using a Hash-Based method with Transaction Trimming for Mining Associations, IEEE Transactions on Knowledge and Data Engineering 9 (1997), 813-825.
33. P. Pitkow, In Search of Reliable Usage of Data on the WWW, Proceedings of the 6th International WWW Conference, Santa Clara, California, 1997.
34. P. Purdom and D. Gucht, Average Case Performance of the Apriori Algorithm, Technical Report TR529, Indiana University.
35. V.V. Raghavan and A. Hafez, Dynamic Data Mining, Proc. 13th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE New Orleans, Louisiana, June 2000, 220-229.
36. S. Ramaswamy, S. Mahajan and A. Silberschatz, On the Discovery of Interesting Patterns in Association Rules, Very Large Databases (1998), 368-379.
37. R. Rastogi and K. Shim, Mining Optimized Association Rules with Categorical and Numeric Attributes, IEEE Transactions on Knowledge nad Data Engineering 14 (2002), 29-50.
38. A. Rozsypal, Association Mining in Time-Varying Domains, Doctoral Dissertaion at the University of Louisiana at Lafayette, Center for Advanced Computer Studies, 2003.
39. A. Rozsypal and M. Kubat, Association Mining in Gradually Changing Domains Proceedings of the 16th International Florida Artificial Intelligence Research Society Conference, (FLAIRS-2003) (366-370), St.Augustin, Florida, May 11-14, 2003.
40. S.S. Shapiro and M.B. Wilk, An Analysis of Variance Test of Normality, Biometrika 52 (1965), 591-611.
41. R. Srikant and R. Agrawal, Mining Generalized Association Rules, Proceedings of the 21th International Conference on Very Large Databases, 1995, 407-419.
42. H. Toivonen, Sampling Large Databases for Association Mining, Proceedings of the 22nd International Conference on Very Large Databases, Mumbay, India, 1996, 134-145.
43. Q. Wei and G. Chen, Mining Generalized Association Rules with Fuzzy Taxonomic Structures, Proceedings of the North America Fuzzy Information Processing Society, New York, 1999.
44. G. Widmer, Tracking Concept Drift through Meta-Learning, Machine Learning 27 (1997), 259-286.
45. G.Widmer and M. Kubat, Learning in the Presence of Concept Drift and Hidden Contexts, Machine Learning 23 (1996), 69-101.
46. G. Widmer and M. Kubat, eds, Machine Learning, 32, No. 2., Special Issue on Concept Drift and Learning in Context-Sensitive Domains, 1998.