A novel algorithm for frequent itemset mining in data warehouses

被引：3
|
作者：
徐利军
谢康林
机构：
[1] Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200030 China
[2] Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200030 China
来源：
Journal of Zhejiang University-Science A(Applied Physics & Engineering) | 2006年 / 02期
关键词：
Frequent itemset; Close itemset; Star schema; Dimension table; Fact table;
D O I：
暂无
中图分类号：
TP311.13 [];
学科分类号：
1201 ;
摘要：
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 al- gorithm outperforms state-of-the-art single-table algorithms.
引用
收藏
页码：216 / 224
页数：9
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