A discretization algorithm of numerical attributes for digital library evaluation based on data mining technology

Zhao, Y; Niu, Z; Peng, X; Dai, L

A discretization algorithm of numerical attributes for digital library evaluation based on data mining technology

Zhao, Y Niu, Z Peng, X

Dai, L

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Publication Type:: Journal Article
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, 7008 LNCS pp. 70 - 76
Issue Date:: 2011-11-09

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Field	Value	Language
dc.contributor.author	Zhao, Y	en_US
dc.contributor.author	Niu, Z	en_US
dc.contributor.author	Peng, X https://orcid.org/0000-0002-8901-1472	en_US
dc.contributor.author	Dai, L	en_US
dc.date.issued	2011-11-09	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, 7008 LNCS pp. 70 - 76	en_US
dc.identifier.isbn	9783642248252	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/116390
dc.description.abstract	We present here a discretization algorithm for numerical attributes of digital collections. In our research data mining technology is imported into digital library evaluation to provide a better decision-making support. But data prediction algorithms work not well based on the traditional discretization method during the data mining process. The reason is that numerical attributes of digital collections are complicated and not in the same scale of distribution distance. We study the characteristic of numerical attributes and put forward a discretization method based on the Z-score idea of mathematical statistics. This algorithm can reflect the dynamic semantic distance for different numerical attributes and significantly enhance the precision rate and recall rate of data prediction algorithms. Furthermore a 'nonlinear conditional relationship' among attributes of digital collections is discovered during the study of discretization algorithm and impacts the actual application result of traditional data mining algorithms. © 2011 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-24826-9_12	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A discretization algorithm of numerical attributes for digital library evaluation based on data mining technology	en_US
dc.type	Journal Article
utslib.citation.volume	7008 LNCS	en_US
utslib.for	080201 Analysis of Algorithms and Complexity	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	7008 LNCS	en_US

Abstract:

We present here a discretization algorithm for numerical attributes of digital collections. In our research data mining technology is imported into digital library evaluation to provide a better decision-making support. But data prediction algorithms work not well based on the traditional discretization method during the data mining process. The reason is that numerical attributes of digital collections are complicated and not in the same scale of distribution distance. We study the characteristic of numerical attributes and put forward a discretization method based on the Z-score idea of mathematical statistics. This algorithm can reflect the dynamic semantic distance for different numerical attributes and significantly enhance the precision rate and recall rate of data prediction algorithms. Furthermore a 'nonlinear conditional relationship' among attributes of digital collections is discovered during the study of discretization algorithm and impacts the actual application result of traditional data mining algorithms. © 2011 Springer-Verlag.

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http://hdl.handle.net/10453/116390