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Automatic Item Weight Generation for Pattern Mining and its Application

Automatic Item Weight Generation for Pattern Mining and its Application

Yun Sing Koh, Russel Pears, Gillian Dobbie
Copyright: © 2011 |Volume: 7 |Issue: 3 |Pages: 20
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781613506356|DOI: 10.4018/jdwm.2011070102
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MLA

Koh, Yun Sing, et al. "Automatic Item Weight Generation for Pattern Mining and its Application." IJDWM vol.7, no.3 2011: pp.30-49. http://doi.org/10.4018/jdwm.2011070102

APA

Koh, Y. S., Pears, R., & Dobbie, G. (2011). Automatic Item Weight Generation for Pattern Mining and its Application. International Journal of Data Warehousing and Mining (IJDWM), 7(3), 30-49. http://doi.org/10.4018/jdwm.2011070102

Chicago

Koh, Yun Sing, Russel Pears, and Gillian Dobbie. "Automatic Item Weight Generation for Pattern Mining and its Application," International Journal of Data Warehousing and Mining (IJDWM) 7, no.3: 30-49. http://doi.org/10.4018/jdwm.2011070102

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Abstract

Association rule mining discovers relationships among items in a transactional database. Most approaches assume that all items within a dataset have a uniform distribution with respect to support. However, this is not always the case, and weighted association rule mining (WARM) was introduced to provide importance to individual items. Previous approaches to the weighted association rule mining problem require users to assign weights to items. In certain cases, it is difficult to provide weights to all items within a dataset. In this paper, the authors propose a method that is based on a novel Valency model that automatically infers item weights based on interactions between items. The authors experiment shows that the weighting scheme results in rules that better capture the natural variation that occurs in a dataset when compared with a miner that does not employ a weighting scheme. The authors applied the model in a real world application to mine text from a given collection of documents. The use of item weighting enabled the authors to attach more importance to terms that are distinctive. The results demonstrate that keyword discrimination via item weighting leads to informative rules.

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