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A causal analysis for the expenditure data of business travelers
Determining the causal relation among attributes in a domain
is a key task in the data mining and knowledge discovery. In this
paper, we applied a causal discovery algorithm to the business traveler
expenditure survey data [1]. A general class of causal models is adopted in
this paper to discover the causal relationship among continuous and discrete variables. All those factors which have direct effect on the expense
pattern of travelers could be detected. Our discovery results reinforced
some conclusions of the rough set analysis and found some new conclusions which might significantly improve the understanding of expenditure behaviors of the business traveler.
is a key task in the data mining and knowledge discovery. In this
paper, we applied a causal discovery algorithm to the business traveler
expenditure survey data [1]. A general class of causal models is adopted in
this paper to discover the causal relationship among continuous and discrete variables. All those factors which have direct effect on the expense
pattern of travelers could be detected. Our discovery results reinforced
some conclusions of the rough set analysis and found some new conclusions which might significantly improve the understanding of expenditure behaviors of the business traveler.
History
Journal
Lecture notes in computer scienceVolume
LNAI 4632Pagination
545 - 552Publisher
SpringerLocation
GermanyISSN
0302-9743Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2007, Springer-VerlagUsage metrics
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