Title :
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
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;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2012 International Conference on
Conference_Location :
Xian
Print_ISBN :
978-1-4673-1484-8
DOI :
10.1109/ICMLC.2012.6358892
