An algorithm for mining maximal frequent itemsets without candidate generation

被引：0
|
作者：
Li Haiwen [1 ]
Yang Li [1 ]
Hong De [1 ]
机构：
[1] Xian Univ Sci & Technol, Coll Comp Sci & Technol, Xian 710054, Peoples R China
来源：
2011 INTERNATIONAL CONFERENCE ON COMPUTER, ELECTRICAL, AND SYSTEMS SCIENCES, AND ENGINEERING (CESSE 2011) | 2011年
关键词：
Data mining; Association rule; Maximal frequent itemset; Frequent pattern tree;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Discovering maximal frequent itemsets is a key problem in mining association rules. In this paper we present an efficient algorithm MFP-growth(maximal Frequent Pattern growth) based on frequent pattern tree(FP-Tree) for mining maximal frequent itemsets without candidate generation in the mining period, therefore it increases the mining efficiency. Our experimental result shows that MFP-growth has excellent performance in mining maximal frequent itemsets.
引用
收藏
页码：330 / 333
页数：4
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