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CLC number: TP31
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
Cited: 1
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A novel algorithm for frequent itemset mining in data warehouses
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Xu Li-jun, Xie Kang-lin. A novel algorithm for frequent itemset mining in data warehouses[J]. Journal of Zhejiang University Science A, 2006, 7(2): 216-224.
@article{title="A novel algorithm for frequent itemset mining in data warehouses",
author="Xu Li-jun, Xie Kang-lin",
journal="Journal of Zhejiang University Science A",
volume="7",
number="2",
pages="216-224",
year="2006",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A0216"
}
%0 Journal Article
%T A novel algorithm for frequent itemset mining in data warehouses
%A Xu Li-jun
%A Xie Kang-lin
%J Journal of Zhejiang University SCIENCE A
%V 7
%N 2
%P 216-224
%@ 1673-565X
%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A0216
TY - JOUR
T1 - A novel algorithm for frequent itemset mining in data warehouses
A1 - Xu Li-jun
A1 - Xie Kang-lin
J0 - Journal of Zhejiang University Science A
VL - 7
IS - 2
SP - 216
EP - 224
%@ 1673-565X
Y1 - 2006
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2006.A0216
Abstract: Current technology for frequent itemset mining mostly applies to the data stored in a single transaction database. This paper presents a novel algorithm MultiClose for frequent itemset mining in data warehouses. MultiClose respectively computes the results in single dimension tables and merges the results with a very efficient approach. close itemsets technique is used to improve the performance of the algorithm. The authors propose an efficient implementation for star schemas in which their algorithm outperforms state-of-the-art single-table algorithms.
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