A Classification Rule Acquisition Algorithm Based on Constrained Concept Lattice
نویسنده
چکیده
Concept lattice is an effective tool for data analysis. Constrained concept lattice, with the characteristics of higher constructing efficiency, practicability and pertinence, is a new concept lattice structure. For classification rule acquisition, a classification rule acquisition algorithm based on the constrained concept lattice is presented by using the concept of partition support according to the relationship between node’s extent of constrained concept lattice and equivalence partition of data set. The experiment results validate the higher classification efficiency and correctness of the algorithm by taking UCI (University of California Irvine) data sets as the formal contexts.
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