医学应用的并行关联规则挖掘

2011 IEEE 11th International Conference on Bioinformatics and Bioengineering Pub Date : 2011-10-24 DOI:10.1109/BIBE.2011.31

G. Zhang, C. Xu, P. Sheu, H. Yamaguchi

{"title":"医学应用的并行关联规则挖掘","authors":"G. Zhang, C. Xu, P. Sheu, H. Yamaguchi","doi":"10.1109/BIBE.2011.31","DOIUrl":null,"url":null,"abstract":"For real-time applications that consist of massive number of rules, partitioning of the rules to support parallel processing is important. This paper proposes a suite of algorithms called GAPCM for parallel processing of massive number of rules. By considering even distribution, minimal waiting time and minimal inter-processor communication, we propose three algorithms for subnet allocation, and apply these algorithms to association rule mining.","PeriodicalId":391184,"journal":{"name":"2011 IEEE 11th International Conference on Bioinformatics and Bioengineering","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Parallel Association Rule Mining for Medical Applications\",\"authors\":\"G. Zhang, C. Xu, P. Sheu, H. Yamaguchi\",\"doi\":\"10.1109/BIBE.2011.31\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For real-time applications that consist of massive number of rules, partitioning of the rules to support parallel processing is important. This paper proposes a suite of algorithms called GAPCM for parallel processing of massive number of rules. By considering even distribution, minimal waiting time and minimal inter-processor communication, we propose three algorithms for subnet allocation, and apply these algorithms to association rule mining.\",\"PeriodicalId\":391184,\"journal\":{\"name\":\"2011 IEEE 11th International Conference on Bioinformatics and Bioengineering\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE 11th International Conference on Bioinformatics and Bioengineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIBE.2011.31\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE 11th International Conference on Bioinformatics and Bioengineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBE.2011.31","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 6

摘要

对于由大量规则组成的实时应用程序，划分规则以支持并行处理非常重要。本文提出了一套用于并行处理大量规则的GAPCM算法。在考虑均匀分布、最小等待时间和最小处理器间通信的基础上，提出了三种子网分配算法，并将其应用于关联规则挖掘。

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

Parallel Association Rule Mining for Medical Applications

For real-time applications that consist of massive number of rules, partitioning of the rules to support parallel processing is important. This paper proposes a suite of algorithms called GAPCM for parallel processing of massive number of rules. By considering even distribution, minimal waiting time and minimal inter-processor communication, we propose three algorithms for subnet allocation, and apply these algorithms to association rule mining.

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

2011 IEEE 11th International Conference on Bioinformatics and Bioengineering

自引率

0.00%

发文量