Justification and hypothesis selection in data mining

dc.citation.epage	130	en_US
dc.citation.spage	119	en_US
dc.citation.volume	6	en_US
dc.contributor.author	Fan, TF	en_US
dc.contributor.author	Liu, DR	en_US
dc.contributor.author	Liau, CJ	en_US
dc.contributor.department	資訊管理與財務金融系註：原資管所+財金所	zh_TW
dc.contributor.department	Department of Information Management and Finance	en_US
dc.date.accessioned	2014-12-08T15:25:09Z
dc.date.available	2014-12-08T15:25:09Z
dc.date.issued	2005	en_US
dc.description.abstract	Data mining is an instance of the inductive methodology. Many philosophical considerations for induction can also be carried out for data mining. In particular, the justification of induction has been a long-standing problem in epistemology. This article is a recast of the problem in the context of data mining. We formulate the problem precisely in the rough set-based decision logic and discuss its implications for the research of data mining.	en_US
dc.identifier.isbn	3-540-26257-1	en_US
dc.identifier.issn	1860-949X	en_US
dc.identifier.journal	Foundations of Data Mining and Knowledge Discovery	en_US
dc.identifier.uri	https://ir.lib.nycu.edu.tw/handle/11536/17544
dc.identifier.wosnumber	WOS:000232911000007
dc.language.iso	en_US	en_US
dc.title	Justification and hypothesis selection in data mining	en_US
dc.type	Proceedings Paper	en_US

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