| A novel algorithm for frequent itemset mining in data warehouses |
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| 作者姓名： | 徐利军 谢康林 |
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| 作者单位： | Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200030 China，Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200030 China |
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| 基金项目： | We would like to thank Mohammed J. Zaki for providing us the source code for CHARM. |
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| 摘 要： | INTRODUCTION A data warehouse (Inmon, 1996) is an integrated and time-varying database primarily used for the support of decision-making, and integrates volumi- nous data from multiple and independent data sources consisting of operational databases in a common repository for querying and analysis. In terms of data modeling, a data warehouse consists of one or several dimensional models that are composed of a central fact table and a set of surrounding dimension tables each corresponding t…
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| 关 键 词： | 数据仓库 数据挖掘 频率项集 闭项集 |
| 收稿时间： | 2005-06-11 |
| 修稿时间： | 2005-10-22 |
A novel algorithm for frequent itemset mining in data warehouses |
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| Li-jun Xu,Kang-lin Xie.A novel algorithm for frequent itemset mining in data warehouses[J].Journal of Zhejiang University Science,2006,7(2):216-224. |
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| Authors: | Li-jun Xu Kang-lin Xie |
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| Institution: | (1) Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China |
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| 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 al- gorithm outperforms state-of-the-art single-table algorithms. |
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| Keywords: | Frequent itemset Close itemset Star schema Dimension table Fact table |
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