Discovering representative episodal association rules from event sequences using frequent closed episode sets and event constraints

Sherri K. Harms, Jitender Deogun, Jamil Saquer, Tsegaye Tadesse

Research output: Chapter in Book/Report/Conference proceedingConference contribution

36 Citations (Scopus)

Abstract

Discovering association rules from time-series data is an important data mining problem. The number of potential rules grows quickly as the number of items in the antecedent grows. It is therefore difficult for an expert to analyze the rules and identify the useful. An approach for generating representative association rules for transactions that uses only a subset of the set of frequent itemsets called frequent closed itemsets was presented in [6]. We employ formal concept analysis to develop the notion of frequent closed episodes. The concept of representative association rules is formalized in the context of event sequences. Applying constraints to target highly significant rules further reduces the number of rules. Our approach results in a significant reduction of the number of rules generated, while maintaining the minimum set of relevant association rules and retaining the ability to generate the entire set of association rules with respect to the given constraints. We show how our method can be used to discover associations in a drought risk management decision support system and use multiple climatology datasets related to automated weather stations1.

Original languageEnglish (US)
Title of host publicationProceedings - 2001 IEEE International Conference on Data Mining, ICDM'01
Pages603-606
Number of pages4
StatePublished - Dec 1 2001
Event1st IEEE International Conference on Data Mining, ICDM'01 - San Jose, CA, United States
Duration: Nov 29 2001Dec 2 2001

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference1st IEEE International Conference on Data Mining, ICDM'01
CountryUnited States
CitySan Jose, CA
Period11/29/0112/2/01

Fingerprint

Association rules
Formal concept analysis
Climatology
Drought
Decision support systems
Risk management
Data mining
Time series

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Harms, S. K., Deogun, J., Saquer, J., & Tadesse, T. (2001). Discovering representative episodal association rules from event sequences using frequent closed episode sets and event constraints. In Proceedings - 2001 IEEE International Conference on Data Mining, ICDM'01 (pp. 603-606). (Proceedings - IEEE International Conference on Data Mining, ICDM).

Discovering representative episodal association rules from event sequences using frequent closed episode sets and event constraints. / Harms, Sherri K.; Deogun, Jitender; Saquer, Jamil; Tadesse, Tsegaye.

Proceedings - 2001 IEEE International Conference on Data Mining, ICDM'01. 2001. p. 603-606 (Proceedings - IEEE International Conference on Data Mining, ICDM).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harms, SK, Deogun, J, Saquer, J & Tadesse, T 2001, Discovering representative episodal association rules from event sequences using frequent closed episode sets and event constraints. in Proceedings - 2001 IEEE International Conference on Data Mining, ICDM'01. Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 603-606, 1st IEEE International Conference on Data Mining, ICDM'01, San Jose, CA, United States, 11/29/01.
Harms SK, Deogun J, Saquer J, Tadesse T. Discovering representative episodal association rules from event sequences using frequent closed episode sets and event constraints. In Proceedings - 2001 IEEE International Conference on Data Mining, ICDM'01. 2001. p. 603-606. (Proceedings - IEEE International Conference on Data Mining, ICDM).
Harms, Sherri K. ; Deogun, Jitender ; Saquer, Jamil ; Tadesse, Tsegaye. / Discovering representative episodal association rules from event sequences using frequent closed episode sets and event constraints. Proceedings - 2001 IEEE International Conference on Data Mining, ICDM'01. 2001. pp. 603-606 (Proceedings - IEEE International Conference on Data Mining, ICDM).
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