DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yongwook Yoon | - |
dc.contributor.author | Lee, GG | - |
dc.date.accessioned | 2016-03-31T08:52:08Z | - |
dc.date.available | 2016-03-31T08:52:08Z | - |
dc.date.created | 2014-02-10 | - |
dc.date.issued | 2013-03 | - |
dc.identifier.issn | 0306-4573 | - |
dc.identifier.other | 2013-OAK-0000026192 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/16208 | - |
dc.description.abstract | Associative classification methods have been recently applied to various categorization tasks due to its simplicity and high accuracy. To improve the coverage for test documents and to raise classification accuracy, some associative classifiers generate a huge number of association rules during the mining step. We present two algorithms to increase the computational efficiency of associative classification: one to store rules very efficiently, and the other to increase the speed of rule matching, using all of the generated rules. Empirical results using three large-scale text collections demonstrate that the proposed algorithms increase the feasibility of applying associative classification to large-scale problems. (C) 2012 Elsevier Ltd. All rights reserved. | - |
dc.description.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | ELSEVIER | - |
dc.relation.isPartOf | Information Processing & Management | - |
dc.title | Two scalable algorithms for associative text classification | - |
dc.type | Article | - |
dc.contributor.college | 컴퓨터공학과 | - |
dc.identifier.doi | 10.1016/j.ipm.2012.09.003 | - |
dc.author.google | Yoon, Y | - |
dc.author.google | Lee, GG | - |
dc.relation.volume | 49 | - |
dc.relation.issue | 2 | - |
dc.relation.startpage | 484 | - |
dc.relation.lastpage | 496 | - |
dc.contributor.id | 10103841 | - |
dc.relation.journal | Information Processing & Management | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCI | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | Information Processing & Management, v.49, no.2, pp.484 - 496 | - |
dc.identifier.wosid | 000314448400006 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 496 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 484 | - |
dc.citation.title | Information Processing & Management | - |
dc.citation.volume | 49 | - |
dc.contributor.affiliatedAuthor | Lee, GG | - |
dc.identifier.scopusid | 2-s2.0-84886408074 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 9 | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Association rule mining | - |
dc.subject.keywordAuthor | Associative classification | - |
dc.subject.keywordAuthor | Text categorization | - |
dc.subject.keywordAuthor | Large-scale dataset | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Information Science & Library Science | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Information Science & Library Science | - |
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