Multi-relational Bayesian Classification Algorithm with Rough Set

Author

Zhang, Chunying ; Wang, Jing

Author_Institution

Coll. of Sci., Hebei Polytech. Univ., Tangshan, China

Volume

4

fYear

2010

fDate

10-12 Aug. 2010

Firstpage

1565

Lastpage

1568

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on

Conference_Location

Yantai, Shandong

Print_ISBN

978-1-4244-5931-5

Type

conf

DOI

10.1109/FSKD.2010.5569347

Filename

5569347