Rule generation for categorical time series with Markov assumptions

Statistics and Computing

Volumn 21, Issue 1, 2011, Pages 1-16

  Weiß, Christian H ^a  

^a UNIVERSITY OF WÜRZBURG   (Germany)

Author keywords

Categorical time series; Episode mining; Sequential pattern analysis; Variable length Markov model

Indexed keywords

EID: 78650304410     PISSN: 09603174     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11222-009-9141-z     Document Type: Article

References (27)

1. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. 20th Int. Conf. on Very Large Databases, Chile, Santiago, pp. 487-499 (1994).
2. Agrawal, R., Srikant, R.: Mining sequential patterns. In: 11th Int. Conf. on Data Engineering (ICDE '95), Taipei, Taiwan, pp. 3-14 (1995).
3. Barnsley, M. F.: Fractals Everywhere. Academic Press, San Diego (1988).
4. Baumgarten, M., Büchner, A. G., Hughes, J. G.: Tree growth based episode mining without candidate generation. In: Int. Conf. on Artificial Intelligence (IC-AI) 2003, pp. 108-114 (2003).
5. Brejová, B., DiMarco, C., Vinař, T., Hidalgo, S. R., Holguin, G., Patten, C.: Finding patterns in biological sequences. Technical report CS-2000-22, University of Waterloo (2000).
6. Brill, E.: A simple rule-based part of speech tagger. In: Proc. of the 3rd Conf. on Applied Natural Language Processing, ACL, pp. 152-155 (1992).
7. Bühlmann, P.: Model selection for variable length Markov chains and tuning the context algorithm. Ann. Inst. Stat. Math. 52, 287-315 (2000).
8. Bühlmann, P., Wyner, A. J.: Variable length Markov chains. Ann. Stat. 27, 480-513 (1999).
9. Casas-Garriga, G.: Discovering unbounded episodes in sequential data. In: Lavrač, N., et al. (eds.) PKDD 2003. LNAI, vol. 2838, pp. 83-94 (2003).
10. Chudova, D., Smyth, P.: Pattern discovery in sequences under a Markov assumption. In: Proc. 8th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, 2002.
11. Cohen, P. R., Adams, N.: An algorithm for segmenting categorical time series into meaningful episodes. In: IDA01. LNAI, vol. 2189, pp. 197-205 (2001).
12. Göb, R.: Data mining and statistical control-a review and some links. In: Lenz, Wilrich (eds.) Frontiers in Stat. Qual. Control 8, pp. 285-308. Physica-Verlag, Heidelberg (2006).
13. Gwadera, R., Atallah, M. J., Szpankowski, W.: Markov models for identification of significant episodes. In: Proc. 2005 SIAM Int. Conf. on Data Mining (SDM-05), Newport Beach, California, 2005.
14. Huang, K.-Y., Chang, C.-H.: Efficient mining of frequent episodes from complex sequences. Inf. Syst. 33, 96-114 (2008).
15. Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., Verkamo, A. I.: Finding interesting rules from large sets of discovered association rules. In: Adam, et al. (eds.), 3rd Int. Conf. on Information and Knowledge Management, pp. 401-407. ACM Press, New York (1997).
16. Laxman, S., Sastry, P. S.: Discovering frequent episodes and learning hidden Markov models: a formal connection. IEEE Trans. Knowl. Discov. Data Eng. 17(11), 1505-1517 (2005).
17. Laxman, S., Sastry, P. S.: A survey of temporal data mining. Sādhanā 31(2), 173-198 (2006).
18. Mannila, H., Toivonen, H., Verkamo, A. I.: Discovery of frequent episodes in event sequences. Data Mining Knowl. Discov. 1, 259-289 (1997).
19. Padmanabhan, B.: The interestingness paradox in pattern discovery. J. Appl. Stat. 31(8), 1019-1035 (2004).
20. Reiss, R.-D.: A Course on Point Processes. Springer, New York (1993).
21. Rissanen, J.: A universal data compression system. IEEE Trans. Inf. Theory 29(5), 656-664 (1983).
22. Ron, D., Singer, Y., Tishby, N.: The power of amnesia: Learning probabilistic automata with variable memory length. Mach. Learn. 25, 117-149 (1996).
23. Sze, S.-H., Gelfand, M. S., Pevzner, P. A.: Finding weak motifs in DNA sequences. Pac. Symp. Biocomput. 7, 235-246 (2002).
24. Weiß, C. H.: Statistical mining of interesting association rules. Stat. Comput. 18(2), 185-194 (2008).
25. Weiß, C. H., Göb, R.: Discovering patterns in categorical time series using IFS. Comput. Stat. Data Anal. 52(9), 4369-4379 (2008).
26. Weiss, G. M.: Timeweaver: A genetic algorithm for identifying predictive patterns in sequences of events. In: Banzhaf, et al. (eds.) Proc. of the Genetic and Evol. Comp. Conf., pp. 719-725. Morgan Kaufmann, San Francisco (1999).
27. Ye, N., Zhang, Y., Borror, C. M.: Robustness of the Markov-chain model for cyber-attack detection. IEEE Trans. Reliab. 53(1), 116-123 (2004).