Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

被引：0
|
作者：
Jiawei Han
Jian Pei
Yiwen Yin
Runying Mao
机构：
[1] University of Illinois at Urbana-Champaign,
[2] State University of New York at Buffalo,undefined
[3] Simon Fraser University,undefined
[4] Microsoft Corporation,undefined
来源：
Data Mining and Knowledge Discovery | 2004年 / 8卷
关键词：
frequent pattern mining; association mining; algorithm; performance improvements; data structure;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist a large number of patterns and/or long patterns.
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页码：53 / 87
页数：34
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