Title :
Attribute reduction among decision tables by voting
Author_Institution :
Coll. of Math., Phys. & Inf. Eng., Zhejiang Normal Univ., Jinhua
Abstract :
In the paper, we propose a novel method of attribute reduction among decision tables by voting. Different from almost all of other methods of attribute reduction, the method is at higher level of granularity. Its granularity is decision tables but not equivalence classes. The method could be applied into the cases of tremendously large data and dynamical data.
Keywords :
data mining; data reduction; decision tables; rough set theory; very large databases; attribute reduction; decision table granularity; dynamical database; large database; rough set theory; voting theory; Concurrent computing; Databases; Distributed computing; Educational institutions; Information systems; Mathematics; Physics; Rough sets; Set theory; Voting;
Conference_Titel :
Granular Computing, 2008. GrC 2008. IEEE International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4244-2512-9
Electronic_ISBN :
978-1-4244-2513-6
DOI :
10.1109/GRC.2008.4664669
