Mining data association based on a revised FP-growth algorithm

Author

Wang, Lei ; Fan, Xing-juan ; Xing-Long Liu ; Zha, Huan

Author_Institution

Agric. Univ. of Hebei, Baoding, China

Volume

1

fYear

2012

fDate

15-17 July 2012

Firstpage

91

Lastpage

95

Abstract

This paper introduces a new weighted Apriori based on a revised FP-growth algorithm to mine association rules in a relational database. The new algorithm is acquired by revising the search mechanism of the well known Apriori weighted multidimensional data mining algorithm which searches for candidate item sets by repeatedly scanning in the database. The effectiveness of our proposed algorithm is verified through a real application of mining in the student achievement database.

Keywords

data mining; educational administrative data processing; relational databases; sensor fusion; Apriori weighted multidimensional data mining algorithm; FP-growth algorithm; association rules mining; candidate item sets; data association mining; relational database; search mechanism; student achievement database; Abstracts; Data mining; Databases; Apriori algorithm; Association rules; Data Mining; FP-growth algorithm; Multi-dimensional Association rules;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2012 International Conference on

Conference_Location

Xian

ISSN

2160-133X

Print_ISBN

978-1-4673-1484-8

Type

conf

DOI

10.1109/ICMLC.2012.6358892

Filename

6358892