{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T15:46:42Z","timestamp":1769356002632,"version":"3.49.0"},"reference-count":47,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2018,1,15]],"date-time":"2018-01-15T00:00:00Z","timestamp":1515974400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["K"],"published-print":{"date-parts":[[2018,2,23]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>Customer lifetime value (CLV) scoring is highly effective when applied to marketing databases. Some researchers have extended the traditional association rule problem by associating a weight with each item in a transaction. However, studies of association rule mining have considered the relative benefits or significance of \u201citems\u201d rather than \u201ctransactions\u201d belonging to different customers. Because not all customers are financially attractive to firms, it is crucial that their profitability be determined and that transactions be weighted according to CLV. This study aims to discover association rules from the CLV perspective.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>This study extended the traditional association rule problem by allowing the association of CLV weight with a transaction to reflect the interest and intensity of customer values. Furthermore, the authors proposed a new algorithm, frequent itemsets of CLV weight (FICLV), to discover frequent itemsets from CLV-weighted transactions.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>Experimental results from the survey data indicate that the proposed FICLV algorithm can discover valuable frequent itemsets. Moreover, the frequent itemsets identified using the FICLV algorithm outperform those discovered through conventional approaches for predicting customer purchasing itemsets in the coming period.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This study is the first to introduce the optimum approach for discovering frequent itemsets from transactions through considering CLV.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/k-03-2016-0042","type":"journal-article","created":{"date-parts":[[2018,1,15]],"date-time":"2018-01-15T07:56:17Z","timestamp":1516002977000},"page":"441-457","source":"Crossref","is-referenced-by-count":8,"title":["Knowledge acquisition of association rules from the customer-lifetime-value perspective"],"prefix":"10.1108","volume":"47","author":[{"given":"Cheng-Hsiung","family":"Weng","sequence":"first","affiliation":[]},{"given":"Tony 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