Mining Temporal Features in Association Rules-Reference-Cited by-同舟云学术

Mining Temporal Features in Association Rules

Published:1999 Issue: Volume: Page:295-300
ISSN:0302-9743
Container-title:Principles of Data Mining and Knowledge Discovery
language:
Short-container-title:

Author:

Chen Xiaodong,Petrounias Ilias

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-540-48247-5_33

Reference4 articles.

1. Agrawal, R., Imielinski, T., Swami, A.: Mining Associations between Sets of Items in Massive Databases. In: Proceedings of ACM SIGMOD International Conference on Management of Data, Washington D.C (May 1993)

2. Chen, X., Petrounias, I., Heathfield, H.: Discovering Temporal Association Rules in Temporal Databases. In: Proceedings of International Workshop on Issues and Applications of Database Technology (IADT 1998), Berlin, Germany (July 1998)

3. Lecture Notes in Computer Science;X. Chen,1998

4. Ramaswamy, S., Mahajan, S., Silberschatz, A.: On the Discovery of Interesting Patterns in Association Rules. In: Proceedings of 24th VLDB Conference, New York, pp. 368–379 (1998)

Cited by 17 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Temporal association rule mining: An overview considering the time variable as an integral or implied component;WIREs Data Mining and Knowledge Discovery;2020-04-27

2. On Mining Temporal Patterns in Dynamic Graphs, and Other Unrelated Problems;Studies in Computational Intelligence;2017-11-27

3. SRIHASS - a similarity measure for discovery of hidden time profiled temporal associations;Multimedia Tools and Applications;2017-09-21

4. On searching and indexing sequences of temporal intervals;Data Mining and Knowledge Discovery;2017-01-16

5. Finding the longest common sub-pattern in sequences of temporal intervals;Data Mining and Knowledge Discovery;2015-02-19