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

页码：330 / 333

页数：4