Please use this identifier to cite or link to this item: https://doi.org/10.1023/A:1021931008240
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dc.titleScoring the data using association rules
dc.contributor.authorLiu, B.
dc.contributor.authorMa, Y.
dc.contributor.authorWong, C.K.
dc.contributor.authorYu, P.S.
dc.date.accessioned2013-07-04T07:34:36Z
dc.date.available2013-07-04T07:34:36Z
dc.date.issued2003
dc.identifier.citationLiu, B., Ma, Y., Wong, C.K., Yu, P.S. (2003). Scoring the data using association rules. Applied Intelligence 18 (2) : 119-135. ScholarBank@NUS Repository. https://doi.org/10.1023/A:1021931008240
dc.identifier.issn0924669X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/39129
dc.description.abstractIn many data mining applications, the objective is to select data cases of a target class. For example, in direct marketing, marketers want to select likely buyers of a particular product for promotion. In such applications, it is often too difficult to predict who will definitely be in the target class (e.g., the buyer class) because the data used for modeling is often very noisy and has a highly imbalanced class distribution. Traditionally, classification systems are used to solve this problem. Instead of classifying each data case to a definite class (e.g., buyer or non-buyer), a classification system is modified to produce a class probability estimate (or a score) for the data case to indicate the likelihood that the data case belongs to the target class (e.g., the buyer class). However, existing classification systems only aim to find a subset of the regularities or rules that exist in data. This subset of rules only gives a partial picture of the domain. In this paper, we show that the target selection problem can be mapped to association rule mining to provide a more powerful solution to the problem. Since association rule mining aims to find all rules in data, it is thus able to give a complete picture of the underlying relationships in the domain. The complete set of rules enables us to assign a more accurate class probability estimate to each data case. This paper proposes an effective and efficient technique to compute class probability estimates using association rules. Experiment results using public domain data and real-life application data show that in general the new technique performs markedly better than the state-of-the-art classification system C4.5, boosted C4.5, and the Naive Bayesian system.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1023/A:1021931008240
dc.sourceScopus
dc.subjectAssociation rules
dc.subjectClassifications
dc.subjectData mining
dc.subjectScoring
dc.subjectTarget selection
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1023/A:1021931008240
dc.description.sourcetitleApplied Intelligence
dc.description.volume18
dc.description.issue2
dc.description.page119-135
dc.description.codenAPITE
dc.identifier.isiut000180391800001
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