An Efficient Distributed Algorithm for Mining Association Rules

Author

Zhao, Yan ; Yao, Yong ; Liu, Zhijng

Author_Institution

Xidian Univ., Xi´´an

Volume

3

fYear

2007

fDate

24-27 Aug. 2007

Firstpage

41

Lastpage

44

Abstract

Modern organizations are geographically distributed. Using the traditional centralized association rule mining to discover useful patterns in such distributed system is not always feasible because merging data sets from different sites into a centralized site incurs huge network communication and time costs. This paper presents an efficient distributed association rule mining (ED-ARM) algorithm to fast find the large itemsets over the distributed transaction database system. Our performance study shows that ED-ARM has a superior performance over the algorithms of CD and FDM.

Keywords

data mining; distributed algorithms; distributed databases; merging; distributed algorithm; distributed transaction database system; efficient distributed association rule mining; merging data sets; time costs; Association rules; Computer science; Costs; Data analysis; Data mining; Distributed algorithms; Itemsets; Merging; Partitioning algorithms; Transaction databases;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on

Conference_Location

Haikou

Print_ISBN

978-0-7695-2874-8

Type

conf

DOI

10.1109/FSKD.2007.151

Filename

4406198