A high efficient AprioriTid algorithm for mining association rule

Author

Li, Zhi-Chao ; He, Pi-Lian ; Lei, Ming

Author_Institution

Sch. of Electron. Inf. Eng., Tianjin Univ., China

Volume

3

fYear

2005

fDate

18-21 Aug. 2005

Firstpage

1812

Abstract

Mining association rule is one of the common forms in data mining, in which the critical problem is to gain the frequent itemsets efficiently. The classical Apriori and AprioriTid algorithm, which are used to construct the frequent itemset, are analyzed in this paper. Author finds out that there too many data due to those items repeatedly saved in the AprioriTid algorithm. On the basis of analysis, we give a theorem of the itemset whose support is less than minsup in C_k-1 is useless in C_k-1. Then, HEA algorithm based on the theorem is offered. The experiments show that the new algorithm is more effective in decreasing data size and execution times than AprioriTid algorithm.

Keywords

data analysis; data mining; AprioriTid algorithm; HEA algorithm; association rule mining; data mining; knowledge discovery in databases; Algorithm design and analysis; Association rules; Data analysis; Data engineering; Data mining; Electronic mail; Helium; Itemsets; Logic; Transaction databases; AprioriTid algorithm; Association rule; HEA algorithm; KDD; date mining;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on

Conference_Location

Guangzhou, China

Print_ISBN

0-7803-9091-1

Type

conf

DOI

10.1109/ICMLC.2005.1527239

Filename

1527239