Effective product assignment based on association rule mining in retail
DC Field | Value | Language |
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dc.contributor.author | Ahn, Kwang-Il | - |
dc.date.accessioned | 2022-07-16T13:02:29Z | - |
dc.date.available | 2022-07-16T13:02:29Z | - |
dc.date.created | 2021-05-12 | - |
dc.date.issued | 2012-11 | - |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/164357 | - |
dc.description.abstract | Much academic research has been conducted about the process of association rule mining. More effort is now required for practical application of association rules in various commercial fields. A potential application of association rule mining is the problem of product assignment in retail. The product assignment problem involves how to most effectively assign items to sites in retail stores to grow sales. Effective product assignment facilitates cross-selling and convenient shopping for customers to promote maximum sales for retailers. However, little practical research has been done to address the issue. The current study approaches the product assignment problem using association rule mining for retail environments. There are some barriers to overcome in applying association rule mining to the product assignment problem for retail. This study conducts some generalizing to overcome drawbacks caused by the short lifecycles of current products. As a measure of cross-selling, lift is used to compare the effectiveness of various assignments for products. The proposed algorithm consists of three processes, which include mining associations among items, nearest neighbor assignments, and updating assignments. The algorithm was tested on synthetic databases. The results show very effective product assignment in terms of the potential for cross-selling to drive maximum sales for retailers. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.title | Effective product assignment based on association rule mining in retail | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ahn, Kwang-Il | - |
dc.identifier.doi | 10.1016/j.eswa.2012.04.086 | - |
dc.identifier.scopusid | 2-s2.0-84864437528 | - |
dc.identifier.wosid | 000307796300026 | - |
dc.identifier.bibliographicCitation | EXPERT SYSTEMS WITH APPLICATIONS, v.39, no.16, pp.12551 - 12556 | - |
dc.relation.isPartOf | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.citation.title | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.citation.volume | 39 | - |
dc.citation.number | 16 | - |
dc.citation.startPage | 12551 | - |
dc.citation.endPage | 12556 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Operations Research & Management Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.subject.keywordPlus | FREQUENT | - |
dc.subject.keywordAuthor | Data mining | - |
dc.subject.keywordAuthor | Association rules | - |
dc.subject.keywordAuthor | Cross-selling | - |
dc.subject.keywordAuthor | Product assignment | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0957417412006987?via%3Dihub | - |
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