AttributeOriented Induction Algorithm Based on Quantitative Extended Concept Lattice
WANG DeXing1,2, HU XueGang1, L IU XiaoPing1, HUANG DongMei2
1.Department of Computer Science and Technology, Hefei University of Technology, Hefei 230009 2.College of Information Technology, Shanghai Fisheries University, Shanghai 200090
Abstract In knowledge discovery in databases (KDD), users show much interest in highlevel, general and reductive information. Attribute oriented induction (AOI), which generally takes the statistical information from original data into account, has been commonly used in data reduction. However, attributeoriented algorithm based on quantitative concept lattice can finish induction with multilevel and multiattribute by using concept ascension according to the Hasse diagram of the quantitative extended concept lattice. Compared with AOI, the generalization path of the proposed algorithm is not unique. The proper generalization paths and thresholds on the Hasse diagram of quantitative extended concept lattice could be found easily. The required reasonable results are gotten, and different granular knowledge is provided for users.
WANG DeXing,HU XueGang,L IU XiaoPing等. AttributeOriented Induction Algorithm Based on Quantitative Extended Concept Lattice[J]. , 2007, 20(6): 843-848.
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