TY - CHAP
T1 - A Rough Set Approach to Building Association Rules and Its Applications
AU - Watada, Junzo
AU - Kawaura, Takayuki
AU - Li, Hao
PY - 2011
Y1 - 2011
N2 - Data mining is a process or method of finding information, evidence, insight, knowledge and hypotheses in a huge database, such as marketing data. Recently, the association rule presented by R. Agrawal in 1983 has been used to rapidly expand a data mining method. This method is general and flexible and can be applied to both general data analysis and very wide surveys. In addition, the rules for this method are complicated. On the other hand, when the support value is minimal and the confidence value is high, the obtained value is already known and trivial. A breakthrough method is needed. The objective of this paper is to present a rough set model to overcome such issues. Employing the rough set model, we analyzed three different scales of databases and compared the results of simulation experiments using proposed and conventional models. The rough set model obtained an efficient number of association rules and usually took less computation time.
AB - Data mining is a process or method of finding information, evidence, insight, knowledge and hypotheses in a huge database, such as marketing data. Recently, the association rule presented by R. Agrawal in 1983 has been used to rapidly expand a data mining method. This method is general and flexible and can be applied to both general data analysis and very wide surveys. In addition, the rules for this method are complicated. On the other hand, when the support value is minimal and the confidence value is high, the obtained value is already known and trivial. A breakthrough method is needed. The objective of this paper is to present a rough set model to overcome such issues. Employing the rough set model, we analyzed three different scales of databases and compared the results of simulation experiments using proposed and conventional models. The rough set model obtained an efficient number of association rules and usually took less computation time.
KW - association rule
KW - data mining
KW - Rough set
UR - http://www.scopus.com/inward/record.url?scp=84885447838&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84885447838&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-19820-5_10
DO - 10.1007/978-3-642-19820-5_10
M3 - Chapter
AN - SCOPUS:84885447838
SN - 9783642198199
VL - 13
T3 - Intelligent Systems Reference Library
SP - 203
EP - 218
BT - Intelligent Systems Reference Library
ER -