A novel mining Method of Global Negative Association Rules in Multi-Database

Author

Li, Hong ; Shen, Yijun ; Hu, Xuegang

Author_Institution

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

Volume

1

fYear

2009

fDate

20-22 Nov. 2009

Firstpage

392

Lastpage

396

Abstract

Mining negative association rules in multi-database has attracted more and more attention. Most existing research focuses on unifying all negative rules discovered from different single databases into a single view. This paper presents a novel method for mining global negative association rules in multi-database. This method produces some infrequent itemsets of potential interest by scanning constructed multi-database frequent pattern tree, and extracts negative association rules of interest according to the proposed correlation model from multi-database. Experimental results show the effectiveness and efficiency of the proposed algorithm.

Keywords

data mining; database management systems; correlation model; global negative association rule mining; itemsets; multidatabase frequent pattern tree; Application software; Association rules; Computer science; Data mining; Information processing; Intelligent networks; Itemsets; Laboratories; Tires; Transaction databases; association rules; global negative associations; multi-database mining;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-4754-1

Electronic_ISBN

978-1-4244-4738-1

Type

conf

DOI

10.1109/ICICISYS.2009.5357816

Filename

5357816