An Algorithm Research for Distributed Association Rules Mining with Constraints Based on Sampling

Author

Li, Hong ; Chen, Song-qiao ; Du, Jian-feng ; Yi, Li-jun ; Xiao, Wei

Author_Institution

Sch. of Inf. Sci. & Eng., Central South Univ., Changsha

Volume

1

fYear

2006

fDate

17-19 July 2006

Firstpage

478

Lastpage

483

Abstract

An algorithm for distributed mining association rules with constraints called DMCASE is presented using Sampling and constraint-based Eclat algorithm. At each database site, sampling algorithm and constraint-based Eclat algorithm are implemented. And the local frequent itemsets satisfying constraints are developed. They then are combined to global frequent itemsets satisfying constraints based on inductive learning method. DMCASE algorithm scans the whole database only once. It is also an algorithm with high efficiency. Results from our experiments show that the algorithm is an effective way to resolve the problem of distributed mining association rules with constraints

Keywords

data mining; distributed processing; DMCASE; constraint-based Eclat algorithm; distributed association rules mining; sampling algorithm; Association rules; Data mining; Distributed computing; Frequency; Itemsets; Learning systems; Partitioning algorithms; Sampling methods; Testing; Transaction databases; Association Rules with Constraints; Data Mining; Sampling;

fLanguage

English

Publisher

ieee

Conference_Titel

Cognitive Informatics, 2006. ICCI 2006. 5th IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

1-4244-0475-4

Type

conf

DOI

10.1109/COGINF.2006.365534

Filename

4216451