DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim K.-j. | ko |
dc.contributor.author | Ahn H. | ko |
dc.date.accessioned | 2013-03-06T19:50:45Z | - |
dc.date.available | 2013-03-06T19:50:45Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 2008 | - |
dc.identifier.citation | EXPERT SYSTEMS WITH APPLICATIONS, v.34, no.2, pp.1200 - 1209 | - |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.uri | http://hdl.handle.net/10203/88228 | - |
dc.description.abstract | The Internet is emerging as a new marketing channel, so understanding the characteristics of online customers' needs and expectations is considered a prerequisite for activating the consumer-oriented electronic commerce market. In this study, we propose a novel clustering algorithm based on genetic algorithms (GAs) to effectively segment the online shopping market. In general, GAs are believed to be effective on NP-complete global optimization problems, and they can provide good near-optimal solutions in reasonable time. Thus, we believe that a clustering technique with GA can provide a way of finding the relevant clusters more effectively. The research in this paper applied K-means clustering whose initial seeds are optimized by GA, which is called GA K-means, to a real-world online shopping market segmentation case. In this study, we compared the results of GA K-means to those of a simple K-means algorithm and self-organizing maps (SOM). The results showed that GA K-means clustering may improve segmentation performance in comparison to other typical clustering algorithms. In addition, our study validated the usefulness of the proposed model as a preprocessing tool for recommendation systems. (C) 2007 Elsevier Ltd. All rights reserved. | - |
dc.language | English | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.subject | ORGANIZING FEATURE MAPS | - |
dc.subject | GENETIC-ALGORITHM | - |
dc.subject | NEURAL-NETWORK | - |
dc.subject | SEGMENTATION | - |
dc.subject | INTEGRATION | - |
dc.subject | FRAMEWORK | - |
dc.subject | COMMERCE | - |
dc.title | A recommender system using GA K-means clustering in an online shopping market | - |
dc.type | Article | - |
dc.identifier.wosid | 000253238900041 | - |
dc.identifier.scopusid | 2-s2.0-36148984621 | - |
dc.type.rims | ART | - |
dc.citation.volume | 34 | - |
dc.citation.issue | 2 | - |
dc.citation.beginningpage | 1200 | - |
dc.citation.endingpage | 1209 | - |
dc.citation.publicationname | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.identifier.doi | 10.1016/j.eswa.2006.12.025 | - |
dc.contributor.localauthor | Ahn H. | - |
dc.contributor.nonIdAuthor | Kim K.-j. | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | recommender system | - |
dc.subject.keywordAuthor | genetic algorithms | - |
dc.subject.keywordAuthor | self-organizing maps | - |
dc.subject.keywordAuthor | market segmentation | - |
dc.subject.keywordAuthor | case-based reasoning | - |
dc.subject.keywordPlus | ORGANIZING FEATURE MAPS | - |
dc.subject.keywordPlus | GENETIC-ALGORITHM | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
dc.subject.keywordPlus | SEGMENTATION | - |
dc.subject.keywordPlus | INTEGRATION | - |
dc.subject.keywordPlus | FRAMEWORK | - |
dc.subject.keywordPlus | COMMERCE | - |
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