MuTIny: A multi-time interval pattern discovery approach to preserve the temporal information in between

Proc. of the IADIS Int. Conf. Intelligent Systems and Agents 2010, Proc. of the IADIS European Conference on Data Mining 2010, Part of the MCCSIS 2010

Volumn , Issue , 2010, Pages 101-106

  Bertone, Alessio ^a   Lammarsch, Tim ^a   Turic, Thomas ^a   Aigner, Wolfgang ^a   Miksch, Silvia ^a   Gärtner, Johannes ^b  

^a DANUBE UNIVERSITY KREMS   (Austria)

^b NONE   (Austria)

Author keywords

Interval mining; Pattern finding; Temporal data mining; Time oriented data

Indexed keywords

APRIORI ALGORITHMS; DATA AND INFORMATION; INTERVAL MINING; PATTERN FINDING; SEQUENCE OF EVENTS; TEMPORAL DATA MINING; TEMPORAL INFORMATION; TIME-INTERVAL PATTERNS; TIME-ORIENTED DATA;

INTELLIGENT AGENTS; INTELLIGENT SYSTEMS;

DATA MINING;

EID: 79955368202     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Aigner W., Bertone A., Miksch S., Schumann H. and Tominski C., Towards a Conceptual Framework for Visual Analytics of Time and Time-Oriented Data, Henderson, S. G.; Biller, B.; Hsieh, M.; Tew, J. D. & Barton, R. R. (eds.), Proceedings of the 2007 Winter Simulation Conference, p. 721-729, Washington, D.C., December, 2007.
2. Bettini C., Wang X.S. and Jajodia S.,. Testing Complex Temporal Relationships Involving Multiple Granularities and Its Application to Data Mining. Proc. of ACM PODS, pp. 68-78, Montreal, 1996.
3. Chen Y. L., Chiang M. C. and Kao M. T., Discovering time-interval sequential patterns in sequence databases, Expert Systems with Applications, 25 (3), pp.343-354, 2003.
4. Fayyad U., Smyth P., Piatetetsky-Shapiro G. and Uthurusamy R., Advances in knowledge discovery and data mining, AAAI Press/The MIT Press, 1996.
5. Frawley W.J., Piatetsky-Shapiro G. and Matheus C.J., Knowledge Discovery in Databases: An Overview, MIT press, 1991.
6. Giannotti F., Nanni M., Pedreschi D., Pinelli F., Mining sequences with temporal annotations, Proceedings of the 2006 ACM symposium on Applied computing, Dijon, France, April 23-27, 2006.
7. Hu Y.-H., Huang T.C., Yang H.-R,and Chen Y.-L, On mining multi-time-interval sequential patterns, Data & Knowledge Engineering, Vol. 68, No. 10, (23 October 2009), pp. 1112-1127, 2009.
8. Magnusson M.S., Discovering Hidden Time Patterns in Behavior: T-Patterns and their Detection. Behavior Research Methods, Instruments and Computers, 32(1): pp. 93-110, 2000.
9. Mannila H., Toivonen H., Verkamo A.I., Discovery of Frequent Episodes in Event Sequences, Data Mining and Knowledge Discovery, Volume 1, Issue 3, pp. 259 - 289, 1997. (Pubitemid 127721244)
10. Mannila H., Toivonen H.and Verkamo A.I., Discovering frequent episodes in sequences. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining. Montreal, Canada, pp. 210-215, 1995.
11. Sacchi L, Larizza C., Combi C. and Bellezzi R., Data mining with Temporal Abstractions: learning rules from time series, Data Mining and Knowledge Discovery archive, Volume 15, Issue 2, pp.: 217-247, 2007. (Pubitemid 47293487)
12. Srikant R. and Agrawal. R, Mining Sequential Patterns: Generalizations and Performance Improvements. In Proc. of the Fifth Int'l Conference on Extending Database Technology (EDBT), Avignon, France, 1996.
13. Vautier, M.-O. Cordier, and R. Quiniou. An inductive database for mining temporal patterns in event sequences. In Workshop on Mining Spatial and Temporal Data, 2000.
14. Yang H.R., Discovering multi-time-interval sequential patterns in sequence database, http://thesis.lib.ncu.edu.tw/ETD-db/ETDsearch-c/view-etd?URN= 91423007 (accessed on 18/02/2010).
15. Yoshida M., Iizuka T., Shiohara H., and M. Ishiguro, Mining sequential patterns including time intervals, In "Data Mining and Knowledge Discovery: Theory, Tools and Technology II (SPIE Conference)", 2000.