DocumentCode :
2741675
Title :
Mining Infrequent Itemsets Based on Multiple Level Minimum Supports
Author :
Xiangjun Dong ; Zhiyun Zheng ; Zhendong Niu ; Qiuting Jia
Author_Institution :
Beijing Inst. of Technol., Beijing
fYear :
2007
fDate :
5-7 Sept. 2007
Firstpage :
528
Lastpage :
528
Abstract :
When we study positive and negative association rules simultaneously, infrequent itemsets become very important because there are many valued negative association rules in them. However, how to discover infrequent itemsets is still an open problem. In this paper, we propose a multiple level minimum supports (MLMS) model to constrain infrequent itemsets and frequent itemsets by giving deferent minimum supports to itemsets with deferent length. We compare the MLMS model with the existing models. We also design an algorithm Apriori_MLMS to discover simultaneously both frequent and infrequent itemsets based on MLMS model. The experimental results and comparisons show the validity of the algorithm.
Keywords :
data mining; Apriori_MLMS; association rules; infrequent itemset mining; multiple level minimum supports; Algorithm design and analysis; Association rules; Computer science; Data mining; Databases; Information science; Itemsets; Taxonomy; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Innovative Computing, Information and Control, 2007. ICICIC '07. Second International Conference on
Conference_Location :
Kumamoto
Print_ISBN :
0-7695-2882-1
Type :
conf
DOI :
10.1109/ICICIC.2007.388
Filename :
4428170
Link To Document :
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