Removing biases in unsupervised learning of sequential patterns

Intelligent Data Analysis

Volumn 11, Issue 5, 2007, Pages 457-480

  Horman, Yoav ^a   Kaminka, Gal A ^a  

^a BAR ILAN UNIVERSITY   (Israel)

Author keywords

Sequence learning; Sequence mining; Sequential patterns

Indexed keywords

EID: 41749092054     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/ida-2007-11503     Document Type: Article

References (24)

1. C.C. Aggarwal and P.S. Yu, A New Framework for Itemset Generation, 1998, 18-24.
2. R. Agrawal, T. Imielinski and A.N. Swami, Mining association rules between sets of items in large databases, in: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, P. Buneman and S. Jajodia, eds, Washington, D.C., 26-28, 1993, pp. 207-216.
3. R. Agrawal and R. Srikant, Mining sequential patterns, in: Eleventh International Conference on Data Engineering, P.S. Yu and A.S.P. Chen, eds, Taipei, Taiwan, 1995. IEEE Computer Society Press, pp. 3-14.
4. M. Bauer, From interaction data to plan libraries: A clustering approach, in: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-99), (Vol. 2), Stockholm, Sweden, August 1999. Morgan-Kaufman Publishers, Inc, 962-967.
5. S. Brin, R. Motwani, J.D. Ullman and S. Tsur, Dynamic itemset counting and implication rules for market basket data, in: SIGMOD 1997, Proceedings ACM SIGMOD International Conference on Management of Data, May 13-15, 1997, Tucson, Arizona, USA, J. Peckham, ed., ACM Press, 05 1997, pp. 255-264.
6. P. Cohen and N. Adams, An algorithm for segmenting categorical time series into meaningful episodes, Lecture Notes in Computer Science, 2189, 2001.
7. W. DuMouchel and D. Pregibon, Empirical bayes screening for multi-item associations, in: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, ACM Press, 2001, pp. 67-76.
8. S. Hettich and S.D. Bay, The uci kdd archive, http://kdd.ics.uci.edu/, 1999.
9. Y. Horman and G.A. Kaminka, Removing statistical biases in unsupervised sequence learning. In Proceedings of Intelligent Data Analysis (IDA-05), Madrid, Spain, 2005.
10. A.E. Howe and P.R. Cohen, Understanding planner behavior, Artificial Intelligence 76(1-2) (1995), 125-166.
11. G.A. Kaminka, M. Fidanboylu, A. Chang and M. Veloso, Learning the sequential behavior of teams from observations, in Proceedings of the 2002 RoboCup Symposium, 2002.
12. M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen and A.I. Verkamo, Finding interesting rules from large sets of discovered association rules, in: Third International Conference on Information and Knowledge Management (CIKM'94), N.R. Adam, B.K. Bhargava and Y. Yesha, eds, ACM Press, 1994, pp. 401-407.
13. T. Lane and C.E. Brodley, Temporal sequence learning and data reduction for anomaly detection, ACM Transactions on Information and System Security 2(3) (1999), 295-331.
14. H. Mannila, H. Toivonen and A.I. Verkamo, Discovery of frequent episodes in event sequences, Data Mining and Knowledge Discovery 1(3) (1997), 259-289.
15. T. Oates and P.R. Cohen, Searching for structure in multiple streams of data, in: Proceedings of the Thirteenth International Conference on Machine Learning, 1996, 346-354.
16. J.S. Park, M.-S. Chen and P.S. Yu, Using a hash-based method with transaction trimming for mining association rules, Knowledge and Data Engineering 9(5) (1997), 813-825.
17. P. Sellers, The theory and computation of evolutionary distances: pattern recognition, Journal of Algorithms 1(1) (1980), 359-373.
18. C. Silverstein, S. Brin and R. Motwani, Beyond market baskets: Generalizing association rules to dependence rules, Data Mining and Knowledge Discovery 2(1) (1998), 39-68.
19. R.R. Sokal and F.J. Rohlf, Biometry: The Principles and Practice of Statistics in Biological Research, W.H. Freeman and Co., New York, 1981.
20. R. Srikant and R. Agrawal, Mining sequential patterns: Generalizations and performance improvements, in: Proc. 5th Int. Conf. Extending Database Technology, EDBT, (Vol. 1057), P.M.G. Apers, M. Bouzeghoub and G. Gardarin, eds, Springer-Verlag, 25-29, 1996, pp. 3-17.
21. R-N. Tan, V. Kumar and J. Srivastava, Selecting the right interestingness measure for association patterns, in: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM Press, 2002, 32-41.
22. M. Zaki, Fast mining of sequential patterns in very large databases. Technical Report 668, University of Rochester Compute Science Department, 1997.
23. M. Zaki, N. Lesh and M. Ogihara, Planmine: Sequence mining for plan failures, in: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, AAAI Press, 1998, 369-374.
24. M.J. Zaki, S. Parthasarathy, M. Ogihara and W. Li, New algorithms for fast discovery of association rules. Technical Report TR651, 1997.