Title :
Multi-relational Bayesian Classification Algorithm with Rough Set
Author :
Zhang, Chunying ; Wang, Jing
Author_Institution :
Coll. of Sci., Hebei Polytech. Univ., Tangshan, China
Abstract :
A Multi-relational Bayesian Classification Algorithm with Rough Set is proposed in this paper. The concept of relational graph used to dynamic choice associative table associated with the target table, and a tuple ID propagation approach is used to solve directly the association rule mining problem with multiple database relations, and the concept of Core in Rough Set is introduced, simplify the associative table. Compared with the traditional algorithm,it improves the accuracy rate. Experimental results show that its running rate is much higher than that of Bayesian Classification Algorithm and Graph_NB Algorithm.
Keywords :
Bayes methods; data mining; graph theory; rough set theory; association rule mining problem; dynamic choice associative table; multirelational Bayesian classification algorithm; relational graph; rough set theory; tuple ID propagation; Accuracy; Algorithm design and analysis; Bayesian methods; Classification algorithms; Heuristic algorithms; Relational databases; Class label propagation; Core; Multi-relational Classification; associative table; relational graph;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-5931-5
DOI :
10.1109/FSKD.2010.5569347
