可擦项集的增量挖掘算法

2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2017-07-01 DOI:10.1109/INISTA.2017.8001172

T. Hong, Kun-Yi Lin, Chun-Wei Lin, Bay Vo

{"title":"可擦项集的增量挖掘算法","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":"{\"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}","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

摘要

EI (Erasable-itemset)挖掘是寻找那些可以被淘汰但不会对工厂利润产生很大影响的项目集。提出了一种可擦项集的增量挖掘算法。它基于快速更新(FUP)方法的概念，该方法最初是为关联挖掘而设计的。实验结果表明，在间歇数据环境下，该算法比批处理算法执行速度更快。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

An incremental mining algorithm for erasable itemsets

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA)

自引率

0.00%

发文量