Finding Causal Patterns from Frequent Itemsets

L. Xiao, Z. Chen, Q. Zhu

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

Abstract

Mining association rules in transaction databases has received much attention in the field of data mining. Although progress has been made on techniques of mining association rules, the results often only indicate the mutual correlative relationships among the frequent items, paying no attention to the directional, or causal relations. For example, when a data set indicates an association between items A and B, it is often not clear whether the access of A caused the access of B, or the converse. In real world applications, however, knowing such causal relations is extremely useful for decision support. People would not only be interested in the facts that A and B are related, but also in the possible sequences and directions among the items. Mining transaction databases for this kind of knowledge offers the potential for deep analysis of business situations and finding strategies of operation. In this paper, we employ a Bayesian approach to mining causal relations from frequent itemsets. The results of our research include two algorithms based on Bayesian statistics model: a serial and diverging connection discovery algorithm (SDCD) and a converging connection discovery algorithm (CCD). Experimental results indicate that the performance of the algorithms is scalable.

Original languageEnglish (US)
Title of host publicationProceedings of the 6th Joint Conference on Information Sciences, JCIS 2002
EditorsJ.H. Caulfield, S.H. Chen, H.D. Cheng, R. Duro, J.H. Caufield, S.H. Chen, H.D. Cheng, R. Duro, V. Honavar
Pages442-445
Number of pages4
StatePublished - Dec 1 2002
EventProceedings of the 6th Joint Conference on Information Sciences, JCIS 2002 - Research Triange Park, NC, United States
Duration: Mar 8 2002Mar 13 2002

Publication series

NameProceedings of the Joint Conference on Information Sciences
Volume6

Conference

ConferenceProceedings of the 6th Joint Conference on Information Sciences, JCIS 2002
CountryUnited States
CityResearch Triange Park, NC
Period3/8/023/13/02

ASJC Scopus subject areas

  • Computer Science(all)

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  • Cite this

    Xiao, L., Chen, Z., & Zhu, Q. (2002). Finding Causal Patterns from Frequent Itemsets. In J. H. Caulfield, S. H. Chen, H. D. Cheng, R. Duro, J. H. Caufield, S. H. Chen, H. D. Cheng, R. Duro, & V. Honavar (Eds.), Proceedings of the 6th Joint Conference on Information Sciences, JCIS 2002 (pp. 442-445). (Proceedings of the Joint Conference on Information Sciences; Vol. 6).