ARMADA - An algorithm for discovering richer relative temporal association rules from interval-based data

Data and Knowledge Engineering

Volumn 63, Issue 1, 2007, Pages 76-90

  Winarko, Edi ^a   Roddick, John F ^a  

^a FLINDERS UNIVERSITY   (Australia)

Author keywords

ARMADA; Interval data; Relative temporal data mining; Temporal association rules

Indexed keywords

CONSTRAINT THEORY; DATA MINING; DATA STRUCTURES; SEMANTICS;

FREQUENT TEMPORAL PATTERNS; RELATIVE TEMPORAL DATA MINING; STATIC RULES; TEMPORAL ASSOCIATION RULES;

ASSOCIATION RULES;

EID: 34250202878     PISSN: 0169023X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.datak.2006.10.009     Document Type: Article

References (20)

1. Roddick J.F., and Spiliopoulou M. A survey of temporal knowledge discovery paradigms and methods. IEEE Transactions on Knowledge and Data Engineering 14 4 (2002) 750-767
2. Agrawal R., and Srikant R. Mining sequential patterns. In: Yu P.S., and Chen A.S.P. (Eds). Proceedings of the 11th International Conference on Data Engineering (ICDE'95) (1995), IEE Computer Society Press, Taipei, Taiwan 3-14
3. Mannila H., and Toivonen H. Discovering generalised episodes using minimal occurrences. In: Simoudis E., Han J., and Fayyad U. (Eds). Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD'96) (1996), AAAI Press, Portland, Oregon 146-151
4. Y. Li, P. Ning, X.S. Wang, S. Jajodia, Discovering calendar-based temporal association rules, in: Proceedings of the 8th International Symposium on Temporal Representation and Reasoning, 2001, pp. 111-118.
5. Ozden B., Ramaswamy S., and Silberschatz A. Cyclic association rules. Proceedings of the 14th International Conference on Data Engineering (ICDE'98) (1998), IEE Computer Society Press, Orlando, Florida, USA 412-421
6. Lu H., Feng L., and Han J. Beyond intratransaction association analysis: mining multidimensional intertransaction association rules. ACM Transactions on Information Systems 18 4 (2000) 423-454
7. Böhlen M.H., Busatto R., and Jensen C.S. Point-versus interval-based temporal data models. Proceedings of the 14th International Conference on Data Engineering (ICDE'98) (1998), IEE Computer Society Press, Orlando, Florida, USA 192-200
8. Allen J. Maintaining knowledge about temporal intervals. Communications of the ACM 26 11 (1983) 832-843
9. Freksa C. Temporal reasoning based on semi-intervals. Artificial Intelligence 54 (1992) 199-227
10. Roddick J.F., and Mooney C.H. Linear temporal sequences and their interpretation using midpoint relationships. IEEE Transactions on Knowledge and Data Engineering 17 1 (2005) 133-135
11. Lin M.Y., and Lee S.Y. Fast discovery of sequential patterns by memory indexing. Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery (DaWaK'02) (2002), Aix-en-Provence, France 150-160
12. Srikant R., and Agrawal R. Mining sequential patterns: Generalizations and performance improvements. Proceedings of the 5th International Conference on Extending Database Technology (1996), Avinon, France 3-17
13. Pei J., Han J., Mortazavi-Asl B., Pinto H., Chen Q., Dayal U., and Hsu M.-C. PrefixSpan: Mining sequential patterns efficiently by prefix projected pattern growth. Proceedings of the 17th International Conference on Data Engineering (ICDE'01) (2001), IEEE Computer Society Press, Heidelberg, Germany 215-224
14. R. Villafane, K.A. Hua, D. Tran, B. Maulik, Mining interval time series, in: Data Warehousing and Knowledge Discovery, 1999, pp. 318-330.
15. Kam P.-S., and Fu A.W.-C. Discovering temporal patterns for interval-based events. In: Kambayashi Y., Mohania M.K., and Tjoa A.M. (Eds). Proceedings of the 2nd International Conference on Data Warehousing and Knowledge Discovery (DaWaK'00) vol. 1874 (2000), Springer, London, UK 317-326
16. R. Agrawal, R. Srikant, Fast algorithms for mining association rules, in: Proceedings of the 20th International Conference on Very Large Data Bases, 1994, pp. 487-499.
17. Zaki M.J. SPADE: An efficient algorithm for mining frequent sequences. Machine Learning Journal 42 1/2 (2001) 31-60
18. F. Höppner, Learning temporal rules from state sequence, in: Proceedings of IJCAI Workshop on Learning from Temporal and Spatial Data, Seattle, USA, 2001, pp. 25-31.
19. G. Koundourakis, B. Theodoulidis, Association rules and evolution in time, in: Proceedings of Methods and Applications of Artificial Intelligence, Second Hellenic Conference on AI, SETN 2002, Thessaloniki, Greece, 2002, pp. 261-272.
20. H. Hamilton, D. Randall, Data mining with calendar attributes, in: J.F. Roddick, K. Hornsby (Eds.), International Workshop on Temporal, Spatial and Spatio-Temporal Data Mining, TSDM2000, vol. 2007 of LNAI, Springer, Lyon, France, 2000.