Article(id=1242117012491669733, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1242116994296778923, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1248105600000, receivedDateStr=2009-07-21, revisedDate=1250092800000, revisedDateStr=2009-08-13, acceptedDate=null, acceptedDateStr=null, onlineDate=1250092800000, onlineDateStr=2009-08-13, pubDate=1250092800000, pubDateStr=2009-08-13, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1250092800000, onlineIssueDateStr=2009-08-13, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1774073373192, creator=sys-migrate, updateTime=1774073373192, updator=sys-migrate, issue=Issue{id=1242116994296778923, tenantId=1146029695717560320, journalId=1146031591421210625, year='2009', volume='27', issue='0915', pageStart='3', pageEnd='140', issueExtLink='null', onlineDate='null', pubDate='1250092800000', pubDateStr='2009-08-13', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=3, issueType=-1, specialIssue=null, createTime=1774073368854, creator='sys-migrate', updateTime=1774073368854, updator='sys-migrate', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=71, endPage=75, ext={EN=ArticleExt(id=1242117015570285490, articleId=1242117012491669733, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Intent Reduction and Classification Rules Mining, columnId=1242116080374710456, journalTitle=Science & Technology Review, columnName=Articles, runingTitle=null, highlight=null, articleAbstract=The concept lattice is accurate and complete in the knowledge representation, and it is an effective tool for data analysis and knowledge discovery. Classification rule mining based on the concept lattice in an efficient way is a challenging task. At present, there are many extraction algorithms for classification rule based on the concept lattice, but the number and form of extracted rule can not achieve satisfactory results. This paper focused on classification rule mining using intent reduction of the formal concept. It proposed an incremental way to compute intent reduction by adding object to the formal context one by one. Through modifying the incremental computation of the intent reduction of concepts, new algorithms are developed to compute the intent reduction, and then definitions of the exact classification rule base and the approximate base are given. On the basis, two algorithms for the exact and the approximate mining bases are designed. In the algorithms, only those concepts that concern the classification rule mining need to be considered in compute their intent reduction. Furthermore, the use of classification rule bases reduces the total number of classification rules that need to be extracted. In order to verify the data mining methods of classification association rules which were put forward in this research, the algorithms mentioned above were implemented by using C++. Finally, empirical experiments on UCI data demonstrated the efficiency of these algorithms in the mining of affirmative classification rules and approximate classification rules using intent reduction., authors=null, authorsList=null, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=handsome, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=86+hN7KNlH6osXVcTNThzQ==, pdfFileSize=664908, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1242117014651732907, articleId=1242117012491669733, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=内涵缩减与分类规则求解, columnId=1146540929516700224, journalTitle=科技导报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=概念格是数据分析与知识提取的一种有效工具，具有精确性和完备性等特点。目前，基于概念格的分类规则提取算法很多，但在提取到规则的数量上和规则的形式上并不能达到令人满意的效果。针对基于概念格的分类规则提取方法进行了研究，在改进内涵缩减的增量式计算方法基础上给出了基于内涵缩减的确定的分类规则和近似的分类规则的提取方法，通过有效限制计算内涵缩减的节点的范围降低了内涵缩减的计算规模，利用分类规则基，降低了需要计算的分类规则的数量，提高了分类规则的提取效率。为验证本研究提出分类关联规则的挖掘方法，用C++实现了上述算法。测试结果表明，本文给出的算法是有效的。, authors=葛斌¹,孟祥瑞², authorsList=葛斌;孟祥瑞, authorCompany=1. 安徽理工大学计算机科学与工程学院2. 安徽理工大学科研处, correspAuthors=null, authorNote=null, correspAuthorsNote=葛斌, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=5CejLhNsSLaL9f3QtGYdDg==, pdfFileSize=664908, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null)}, authors=null, keywords=[Keyword(id=1242117014274245538, tenantId=1146029695717560320, journalId=1146031591421210625, articleId=1242117012491669733, 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