Multiple Data Source Discovery with Group Interaction Approach
Medical researchers seek to identify and predict profit (or effectiveness) potential in a new medicine B against a specified disease by comparing it to an existing medicine A,which has been used to treat the disease for many years,called medicine assessment.Applying traditional data mining techniques to the medicine assessment,one can discover patterns,such as A.X=a → B.Y=b,which are identified at the attribute-value level.These patterns are useful in predicting associated behaviors at the attribute-value level.However,to evaluate B against A,we have to obtain globally useful relations between B and A at an attribute level.Therefore,this paper proposes a group interaction approach for multiple data source discovery.Group interactions include,such as rules,differences,and links between datasets.These group interactions are discovered at the attribute level.For example,R(A.X,B.Y),where R is a relationship,or a predication.Some examples are presented for illustrating the use of the group interaction approach.
Data mining multiple data source mining interaction difference detection
Wu Hao
Liuzhou Railway Vocational Technology College Liuzhou,China,545007
国际会议
杭州
英文
2834-2837
2013-03-22（万方平台首次上网日期，不代表论文的发表时间）