Mining erasable itemsets

In this paper, we introduce a new kind of mining problem - mining erasable itemsets, which is derived from planning products of the manufacturing industry. For this problem, we first present the formal definition of mining erasable itemsets and discuss some basic properties of the problem. Based on...

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Published in2009 International Conference on Machine Learning and Cybernetics Vol. 1; pp. 67 - 73
Main Authors Zhi-Hong Deng, Guo-Dong Fang, Zhong-Hui Wang, Xiao-Ran Xu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
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ISBN9781424437023
1424437024
ISSN2160-133X
DOI10.1109/ICMLC.2009.5212520

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Abstract In this paper, we introduce a new kind of mining problem - mining erasable itemsets, which is derived from planning products of the manufacturing industry. For this problem, we first present the formal definition of mining erasable itemsets and discuss some basic properties of the problem. Based on our analyses, we propose an efficient algorithm, META, for mining erasable itemsets. For evaluating META algorithm, we built three synthetic product databases. The results of applying META algorithm to these synthetic databases show its effectiveness.
AbstractList In this paper, we introduce a new kind of mining problem - mining erasable itemsets, which is derived from planning products of the manufacturing industry. For this problem, we first present the formal definition of mining erasable itemsets and discuss some basic properties of the problem. Based on our analyses, we propose an efficient algorithm, META, for mining erasable itemsets. For evaluating META algorithm, we built three synthetic product databases. The results of applying META algorithm to these synthetic databases show its effectiveness.
Author Xiao-Ran Xu
Guo-Dong Fang
Zhong-Hui Wang
Zhi-Hong Deng
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  surname: Xiao-Ran Xu
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  organization: Key Lab. of Machine Perception (Minist. of Educ.), Peking Univ., Beijing, China
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Snippet In this paper, we introduce a new kind of mining problem - mining erasable itemsets, which is derived from planning products of the manufacturing industry. For...
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StartPage 67
SubjectTerms Cybernetics
Data mining
Forward contracts
Itemsets
Machine learning
Manufacturing
META
Mining erasable itemsets
Mining industry
Mining problem
Pattern analysis
Production facilities
Title Mining erasable itemsets
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