J. Qi, Xinwei Mou, Yue Li, Xiaoquan Chu, Weisong Mu
{"title":"A novel consumer preference mining method based on improved weclat algorithm","authors":"J. Qi, Xinwei Mou, Yue Li, Xiaoquan Chu, Weisong Mu","doi":"10.1108/jec-08-2021-0113","DOIUrl":null,"url":null,"abstract":"\nPurpose\nConventional frequent itemsets mining ignores the fact that the relative benefits or significance of “transactions” belonging to different customers are different in most of the relevant applied studies, which leads to failure to obtain some association rules with lower support but from higher-value consumers. Because not all customers are financially attractive to firms, it is necessary that their values be determined and that transactions be weighted. The purpose of this study is to propose a novel consumer preference mining method based on conventional frequent itemsets mining, which can discover more rules from the high-value consumers.\n\n\nDesign/methodology/approach\nIn this study, the authors extend the conventional association rule problem by associating the “annual purchase amount” – “price preference” (AP) weight with a consumer to reflect the consumer’s contribution to a market. Furthermore, a novel consumer preference mining method, the AP-weclat algorithm, is proposed by introducing the AP weight into the weclat algorithm for discovering frequent itemsets with higher values.\n\n\nFindings\nThe experimental results from the survey data revealed that compared with the weclat algorithm, the AP-weclat algorithm can make some association rules with low support but a large contribution to a market pass the screening by assigning different weights to consumers in the process of frequent itemsets generation. In addition, some valuable preference combinations can be provided for related practitioners to refer to.\n\n\nOriginality/value\nThis study is the first to introduce the AP-weclat algorithm for discovering frequent itemsets from transactions through considering AP weight. Moreover, the AP-weclat algorithm can be considered for application in other markets.\n","PeriodicalId":46489,"journal":{"name":"Journal of Enterprising Communities-People and Places in the Global Economy","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2021-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Enterprising Communities-People and Places in the Global Economy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/jec-08-2021-0113","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 1
Abstract
Purpose
Conventional frequent itemsets mining ignores the fact that the relative benefits or significance of “transactions” belonging to different customers are different in most of the relevant applied studies, which leads to failure to obtain some association rules with lower support but from higher-value consumers. Because not all customers are financially attractive to firms, it is necessary that their values be determined and that transactions be weighted. The purpose of this study is to propose a novel consumer preference mining method based on conventional frequent itemsets mining, which can discover more rules from the high-value consumers.
Design/methodology/approach
In this study, the authors extend the conventional association rule problem by associating the “annual purchase amount” – “price preference” (AP) weight with a consumer to reflect the consumer’s contribution to a market. Furthermore, a novel consumer preference mining method, the AP-weclat algorithm, is proposed by introducing the AP weight into the weclat algorithm for discovering frequent itemsets with higher values.
Findings
The experimental results from the survey data revealed that compared with the weclat algorithm, the AP-weclat algorithm can make some association rules with low support but a large contribution to a market pass the screening by assigning different weights to consumers in the process of frequent itemsets generation. In addition, some valuable preference combinations can be provided for related practitioners to refer to.
Originality/value
This study is the first to introduce the AP-weclat algorithm for discovering frequent itemsets from transactions through considering AP weight. Moreover, the AP-weclat algorithm can be considered for application in other markets.