Robust mining of time intervals with semi-interval partial order patterns

Proceedings of the 10th SIAM International Conference on Data Mining, SDM 2010

Volumn , Issue , 2010, Pages 315-326

  Moerchen, Fabian ^a   Fradkin, Dmitriy ^a  

^a SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EMPIRICAL EVALUATIONS; INTERVAL DATA; ITEMSET MINING; MINING ALGORITHMS; NEW APPROACHES; PARTIAL ORDER; SEQUENTIAL PATTERNS; TIME INTERVAL;

DATA MINING;

EID: 80052392674     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972801.28     Document Type: Conference Paper

References (45)

1. R. Agrawal and R. Srikant. Mining sequential patterns. In Proc. IEEE ICDE, pages 3-14. IEEE Press, 1995.
2. M. Aiello, C. Monz, L. Todoran, and M. Worring. Document understanding for a broad class of documents. Intl. Journal on Document Analysis and Recognition, 5(1):1-16, 2002.
3. J. F. Allen. Maintaining knowledge about temporal intervals. Communications of the ACM, 26(11):832-843, 1983.
4. A. Asuncion and D. Newman. UCI Machine Learning Repository. University of California, Irvine http://www.ics.uci.edu/~mlearn/MLRepository.html.
5. G. Casas-Garriga. Summarizing sequential data with closed partial orders. In Proc. of the 5th SIAM Intl. Conf. on Data Mining (SDM), pages 380-391. SIAM, 2005.
6. Y.-L. Chen and T.-K. Huang. Discovering fuzzy time-interval sequential patterns in sequence databases. IEEE Trans. Systems, Man, and Cybernetics, 35(5):959-972, 2005.
7. H. Cheng, X. Yan, J. Han, and C.-W. Hsu. Discriminative frequent pattern analysis for effective classification. In Proc. IEEE ICDE, 2007.
8. P. R. Cohen. Fluent learning: Elucidating the structure of episodes. In Proc. of the 4th Intl. Conf. in Intelligent Data Analysis (IDA), pages 268-277. Springer, 2001.
9. G. Dong and J. Pei. Sequence Data Mining. Morgan Kaufmann, 200y.
10. A. Fern. Learning Models and Formulas of a Temporal Event Logic. PhD thesis, Purdue University, West Lafayette, IN, USA, 2004.
11. C. Freksa. Temporal reasoning based on semi-intervals. Artificial Intelligence, 54(1):199-227, 1992.
12. F. Gianotti, M. Nanni, and D. Pedreschi. Efficient mining of temporally annotated sequences. In Proc. of the 6th SIAM Intl. Conf. on Data Mining (SDM), pages 346-357. SIAM, 2006.
13. G. Guimarães, J. Peter, T. Penzel, and A. Ultsch. A method for automated temporal knowledge acquisition applied to sleep-related breathing disorders. Artificial Intelligence in Medicine, 23(3):211-237, 2001.
14. T. Guyet and R. Quiniou. Mining temporal patterns with quantitative intervals. In Proc. IEEE Intl. Conf. on Data Mining Workshops, pages 218-227. IEEE, 2008.
15. J. Han and M. Kamber. Data Mining - Concepts and Techniques, 2nd edition. Morgan Kaufmann, 2006.
16. Y. Hirate and H. Yamaha. Generalized sequential pattern minin with item intervals. Journal of computers, 1(3):51-60, 2006.
17. F. Hoeppner and A. Topp. Classification based on the trace of variables over time. In Proc. Int. Conf. Intelligent Data Engineering and Automated Learning (IDEAL), pages 739-749. Springer, 2007.
18. F. Höppner. Discovery of temporal patterns - learning rules about the qualitative behaviour of time series. In Proc. of the 5th European Conf. on Principles of Data Mining and Knowledge Discovery (PKDD), pages 192-203. Springer, 2001.
19. M. W. Kadous. Temporal Classification: Extending the Classification Paradigm to Multivariate Time Series. PhD thesis, University of New South Wales, 2002.
20. P.-S. Kam and A.W.-C. Fu. Discovering temporal patterns for interval-based events. In Proc. DaWaK, pages 317-326. Springer, 2000.
21. S. Kempe, J. Hipp, and R. Kruse. Fsmtree: An efficient algorithm for mining frequent temporal patterns. In Proc. Conf. of the Gesellschaft fr Klassifikation, pages 253-260. Springer, 2008.
22. C. Lucchese, S. Orlando, and R. Perego. Fast and memory efficient mining of frequent closed itemsets. IEEE TKDE, 18(1):21-36, 2006.
23. H. Mannila, H. Toivonen, and I. Verkamo. Discovery of frequent episodes in event sequences. In Proc. of the 1st Intl. Conf. on Knowledge Discovery and Data Mining (KDD), pages 210-215. AAAI Press, 1995.
24. J. Mäntyjärvi, J. Himberg, P. Kangas, U. Tuomela, and P. Huuskonen. Sensor signal data set for exploring context recognition of mobile devices. In Proc. of 2nd Int. Conf. on Pervasive Computing (PERVASIVE 2004), pages 18-23. Springer, 2004.
25. F. Mörchen. A better tool than allen's relations for expressing temporal knowledge in interval data. In TDM Workshop, ACM SIGKDD, pages 25-34, 2006.
26. F. Mörchen. Unsupervised pattern mining from symbolic temporal data. SIGKDD Explor. Newsl., 9(1):41-55, 2007.
27. F. Mörchen and A. Ultsch. Optimizing time series discretization for knowledge discovery. In Proc. ACM SIGKDD, pages 660-665. ACM Press, 2005.
28. F. Mörchen and A. Ultsch. Efficient mining of understandable patterns from multivariate interval time series. Data Min. Knowl. Discov., 2007.
29. R. Moskovitch and Y. Shahar. Karmalego - fast time intervals mining. Technical Report 23, ISE-TECH-REP Ben Gurion University, 2009.
30. P. Papaterou, G. Kollios, S. Sclaroff, and D. Gunopou-los. Discovering frequent arrangements of temporal intervals. In Proc. of the 5th IEEE Intl. Conf. on Data Mining (ICDM), pages 354-361, 2005.
31. D. Patel, W. Hsu, and M. Lee. Mining relationships among interval-based events for classification. In Proc. SIGMOD, pages 393-404, 2008.
32. J. Pei, J. Liu, H. Wang, K. Wang, P. S. Yu, and J. Wang. Efficiently mining frequent closed partial orders. In Proc. of the 5th IEEE Intl. Conf. on Data Mining (ICDM), pages 753-756. IEEE Press, 2005.
33. J. Pei, H. Wang, J. Liu, K. Wang, J. Wang, and P. S. Yu. Discovering frequent closed partial orders from strings. IEEE TKDE, 18(11):1467-1481, 2006.
34. C. Rainsford and J. Roddick. Adding temporal semantics to association rules. In Proc. of the 3rd European Conf. on Principles of Data Mining and Knowledge Discovery (PKDD), pages 504-509. Springer, 1999.
35. C. Raïssi, T. Calders, and P. Poncelet. Mining conjunctive sequential patterns. Data Min. Knowl. Discov., 17(1):77-93, 2008.
36. J. F. Roddick and C. H. Mooney. Linear temporal sequences and their interpretation using midpoint relationships. IEEE TKDE, 17(1):133-135, 2005.
37. S. Schockaert, M. De Cock, and E. Kerre. Fuzzyfying allen's temporal relations. IEEE Trans. Fuzzy Systems, 16(2):517-533, 2008.
38. T. Starner, J. Weaver, and A. Pentland. Real-time American Sign Language recognition using desk and wearable computer-based video. IEEE TPAMI, 20(12), 1998.
39. R. Villafane, K. A. Hua, D. Tran, and B. Maulik. Mining interval time series. In Proc. DaWaK, pages 318-330. Springer, 1999.
40. J. Wang and J. Han. BIDE: Efficient mining of frequent closed sequences. In Proc. ICDE, pages 79-90. IEEE Press, 2004.
41. E. Winarko and J. F. Roddick. Armada - an algorithm for discovering richer relative temporal association rules from interval-based data. Data & Knowledge Engineering, 2007.
42. S.-Y. Wu and Y.-L. Chen. Mining nonambiguous temporal patterns for interval-based events. IEEE TKDE, 19(6):742-758, 2007.
43. Z. Xing, J. Pei, G. Dong, and P. S. Yu. Mining sequence classiers for early prediction. In Proc. IEEE ICDM, 2008.
44. M. Yoshida, T. Iizuka, H. Shiohara, and M. Ishiguro. Mining sequential patterns including time intervals. In Proc. of SPIE, volume 4057, pages 213-220, 2000.
45. Q. Zhao and S. Bhowmick. Sequential pattern mining: A survey. Technical report, Nanyang Technichal University, Singapore, 2003.