NDLI: Generating Hierarchical Association Rules with the Use of Bayesian Network

Content Provider	IEEE Xplore Digital Library
Author	Iqbal, K. Asghar, S.
Copyright Year	2009
Abstract	Bayesian Network (BN) is used to build a model using a probability distribution over a set of variables. The benefit of BN is the compact representation of complex problem domains. Also, BN provides decision making, smooth, consistent and flexible applicability in the complex domains. On the other hand, association rules are based on antecedent and consequent part which have condition attributes and decision attributes respectively. Each association rule has a confidence percentage value which shows the rule effectiveness in terms of probability. Thus, association rules and Bayesian Network can be linked up due to probabilistic approach. Therefore, we focus on association rules to develop an automated data mining technique with the use of Bayesian Network. For this purpose, we suggested Associated Rules Binary Symmetric Matrix using K2 (ARBSM-K2) technique in order to generate Hierarchical Association Rules (HAR). This structure shows the relations among the association rules which show more certainty in the hierarchy after maximizing the probability for each association rule treated as a node. Hence, this hierarchy may open new research dimensions to academia community by refining the existing techniques with the latest techniques to further obtain knowledge in any domain. Thus, the significance of the paper is to utilize it for fraud detection from the start to the end showing all steps in sequence.
Starting Page	528
Ending Page	533
File Size	585081
Page Count	6
File Format	PDF
ISBN	9781424450879
DOI	10.1109/NSS.2009.43
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-19
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Symmetric matrices Heuristic algorithms Decision making Bayesian Network Association Rules Probability distribution Transaction databases Association rules Data mining Computer science Bayesian methods Computer security BSM
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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