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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.<\/p>","DOI":"10.4018\/jdwm.2011070102","type":"journal-article","created":{"date-parts":[[2011,10,19]],"date-time":"2011-10-19T12:11:33Z","timestamp":1319026293000},"page":"30-49","source":"Crossref","is-referenced-by-count":11,"title":["Automatic Item Weight Generation for Pattern Mining and its Application"],"prefix":"10.4018","volume":"7","author":[{"given":"Yun Sing","family":"Koh","sequence":"first","affiliation":[{"name":"The University of Auckland, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Russel","family":"Pears","sequence":"additional","affiliation":[{"name":"Auckland University of Technology, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gillian","family":"Dobbie","sequence":"additional","affiliation":[{"name":"The University of Auckland, New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"jdwm.2011070102-0","doi-asserted-by":"crossref","unstructured":"Agrawal, R., Imielinski, T., & Swami, A. 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