NDLI: Relational methodology for data mining and knowledge discovery

Content Provider	IEEE Xplore Digital Library
Author	Vityaev, E. Kovalerchuk, B.
Copyright Year	2005
Description	Author affiliation: Sobolev Inst. of Math., Russian Acad. of Sci., Novosibirsk, Russia (Vityaev, E.)
Abstract	This paper analyses capabilities of machine learning and KDD&DM methods to perform cognitive processes in the form of discovering the domain theories. The concept of cognition of the domain theory is derived for the reprehensive measurement theory (RMT). We show that a relational data mining approach we proposed previously performs cognition of domain theories in accordance with the RMT and produces the relational methodology for analysis of cognitive capabilities of data mining methods. In this methodology a domain theory includes a metadata ontology. This ontology contains various data types formalized in the first-order logic in accordance with the RMT. To represent the knowledge theory we use the concept of the logical empirical theory that is defined in the paper.
Starting Page	725
Ending Page	729
File Size	112138
Page Count	5
File Format	PDF
ISBN	0769524249
ISSN	15294188
DOI	10.1109/DEXA.2005.162
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-22
Publisher Place	Denmark
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Cognition Performance analysis Machine learning Ontologies Humans Mathematics Computer science Logic Measurement units
Content Type	Text
Resource Type	Article

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