Efficient Mining of Strong Negative Association Rules in Multi-Database

Author

Li, Hong ; Hu, Xuegang

Author_Institution

Dept. of Comput. Sci. & Technol., Hefei Univ., Hefei, China

fYear

2009

fDate

11-13 Dec. 2009

Firstpage

1

Lastpage

4

Abstract

Strong negative association rules can reveal irrelevances hidden between frequent itemsets. Existing research has made significant efforts in discovering both positive and negative association rules from single database. This paper presents an efficient method for mining strong negative association rules in multi-database. The method produces some strong negative relational patterns (a kind of infrequent itemsets) by pruning and scanning constructed multi-database frequent pattern tree, and extracts strong negative association rules according to the proposed correlation model. The experimental results show the effectiveness and efficiency of the proposed algorithm.

Keywords

data mining; database management systems; data mining; multidatabase frequent pattern tree; strong negative association rules; Association rules; Computer science; Data mining; Decision making; Information processing; Intelligent networks; Itemsets; Laboratories; Relational databases; Transaction databases;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4507-3

Electronic_ISBN

978-1-4244-4507-3

Type

conf

DOI

10.1109/CISE.2009.5364801

Filename

5364801