{"title":"An incremental mining algorithm for erasable itemsets","authors":"T. Hong, Kun-Yi Lin, Chun-Wei Lin, Bay Vo","doi":"10.1109/INISTA.2017.8001172","DOIUrl":null,"url":null,"abstract":"Erasable-itemset (EI) mining is to find the itemsets that can be eliminated but do not greatly affect the factory's profit. In this paper, an incremental mining algorithm for erasable itemset is proposed. It is based on the concept of the fast-update (FUP) approach, which was originally designed for association mining. Experimental results show that the proposed algorithm executes faster than the batch approach in the intermittent data environment.","PeriodicalId":314687,"journal":{"name":"2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INISTA.2017.8001172","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
Erasable-itemset (EI) mining is to find the itemsets that can be eliminated but do not greatly affect the factory's profit. In this paper, an incremental mining algorithm for erasable itemset is proposed. It is based on the concept of the fast-update (FUP) approach, which was originally designed for association mining. Experimental results show that the proposed algorithm executes faster than the batch approach in the intermittent data environment.