Discovering Temporal Relation Rules Mining from Interval Data

1. J. F. Roddick, M. Spiliopoulou: Temporal data mining: survey and issues, Research Report ACRC-99-007, University of South Australia(1999)

   Google Scholar 

2. J. Allen: Maintaining Knowledge about Temporal Intervals, Comm. Of the ACM, Vol. 26, No. 11, Nov. (1983)

   Google Scholar 

3. J.Y. Lee, K.J. Oh, Keun Ho Ryu: Integration with Spatiotemporal Relationship Operators in SQL, ACM-GIS (1998)

   Google Scholar 

4. Dong Ho Kim, Keun Ho Ryu, Chie Hang Park: Design and implementation of spatiotemporal database query processing system, Journal of systems and software, Dec., (2001)

   Google Scholar 

5. R. Agrawal, R. Srikant: Mining sequential patterns, Int’l. Conf. on Data Engineering, Taipei, Taiwan (1995)

   Google Scholar 

6. X. Chen, I. Petrounias: A framework for temporal data mining, Int’l. Conf. on Database and Expert Systems Applications (1998).

   Google Scholar 

7. C. Rainsford, J. F. Roddick: Temporal data mining in information systems: a model, Australasian Conf. on Information Systems (1996)

   Google Scholar 

8. M. H. Saraee, B. Theodoulidis: Knowledge discovery in temporal databases, IEEE Colloquium on Knowledge Discovery in Databases (1995)

   Google Scholar 

9. R. Srikant, R. Agrawal: Mining sequential patterns: generalisations and performance improvements, In Proc. Int’l. Conf. on Extending Database Technology, Avignon, France, Springer-Verlag(1996)

   Google Scholar 

10. Minos N. Garofalakis, Rajeev Rastogi, Kyuseok Shim: SPIRIT: Sequential Pattern Mining with Regular Expression Constraints, the VLDB Conf., Edinburgh, Scotland, UK (1999)

    Google Scholar 

11. H. Mannila, H. Toivonen: Discovering generalized episodes using minimal occurrences, Int’l Conf. on Knowledge Discovery in Databases and Data Mining (KDD-96), Portland, USA (1996)

    Google Scholar 

12. J. Han, G. Dong, Y. Yin: Efficient Mining of Partial Periodic Patterns in Time Series Database, Int’l. Conf. on Data Engineering, Sydney, Australia (1999)

    Google Scholar 

13. R. Agrawal, King-Ip Lin, Harpreet S. Sawhney, Kyuseok Shim: Fast similarity search in the presence of noise, scaling, and translation in time series databases, the VLDB Conf., Zurich, Switzerland (1995)

    Google Scholar 

14. C. Faloutsos, M. Ranganathan, Y. Manolopoulos: Fast subsequence matching in time-series databases, the ACM SIGMOD Conf. on Management of Data, Minneapolis, USA (1994)

    Google Scholar 

15. B. Ozden, S. Ramaswamy, and A. Silberschatz: Cyclic association rules, Int’l. Conf. on Data Engineering, Orlando, USA (1998)

    Google Scholar 

16. X. Chen, I. Petrounias, H. Heathfield: Discovering temporal association rules in temporal databases, Int’l. Workshop on Issues and Applications of Database Technology (1998)

    Google Scholar 

17. S. Ramaswamy, S. Mahajan, A. Silberschatz: On the discovery of interesting patterns in association rules, the VLDB Conf., New York City, USA (1998)

    Google Scholar 

18. J. M. Ale, G. H. Rossi: An Approach to Discovering Temporal Association Rules, SAC’00, Italy (2000)

    Google Scholar 

19. C. Rainsford: Accommodating Temporal Semantics in Knowledge Discovery and Data Mining, PhD Thesis, University of South Australia (1998)

    Google Scholar 

20. Y. J. Lee: A Data Mining Technique for Discovering Temporal Relation Rules, PhD Thesis, Chungbuk National University (2001)

    Google Scholar 

21. Yun, H., Ha, D., Hwang, B., Ryu, K.: Mining Association Rules on Significant Rare Data using Relative Support. Journal of Systems and Software, 2002 (accepted).

    Google Scholar 

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