id: 05961107
dt: a
an: 05961107
au: Zhao, Xujun
ti: A classification rule acquisition algorithm based on constrained concept
    lattice.
so: Deng, Hepu (ed.) et al., Artificial intelligence and computational
    intelligence. Third international conference, AICI 2011, Taiyuan,
    China, September 24‒25, 2011. Proceedings, Part I. Berlin: Springer
    (ISBN 978-3-642-23880-2/pbk). Lecture Notes in Computer Science 7002.
    Lecture Notes in Artificial Intelligence, 356-363 (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: Constrained Concept Lattice; Classification Rule; Partition Support;
    Background Knowledge; Consistent Node
ci: 
li: doi:10.1007/978-3-642-23881-9_47
ab: Summary: 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.
rv: