{"title":"A text mining method for discovering hidden links","authors":"Guangrong Li, Xiaodan Zhang, Illhoi Yoo, Xiaohua Zhou","doi":"10.1109/GRC.2009.5255095","DOIUrl":null,"url":null,"abstract":"This paper presents a Biomedical Semantic-based Association Rule method that significantly reduces irrelevant connections through semantic filtering. The experiment result shows that compared to traditional association rule-based approach, our approach generates much fewer rules and a lot of these rules represent relevant connections among biological concepts.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"76 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Conference on Granular Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GRC.2009.5255095","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This paper presents a Biomedical Semantic-based Association Rule method that significantly reduces irrelevant connections through semantic filtering. The experiment result shows that compared to traditional association rule-based approach, our approach generates much fewer rules and a lot of these rules represent relevant connections among biological concepts.
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