TY - GEN
T1 - A causal analysis for the expenditure data of business travelers
AU - Law, Chun Hung Roberts
AU - Li, Gang
PY - 2007/12/1
Y1 - 2007/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=38049091296&partnerID=8YFLogxK
M3 - Conference article published in proceeding or book
SN - 9783540738701
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 545
EP - 552
BT - Advanced Data Mining and Applications - Third International Conference, ADMA 2007, Proceedings
T2 - 3rd International Conference on Advanced Data Mining and Applications, ADMA 2007
Y2 - 6 August 2007 through 8 August 2007
ER -