GenMax: An Efficient Algorithm for Mining Maximal Frequent Itemsets

被引：0
|
作者：
Karam Gouda
Mohammed J. Zaki
机构：
[1] Faculty of Science,Department of Mathematics
[2] Rensselaer Polytechnic Institute,Computer Science Department
来源：
Data Mining and Knowledge Discovery | 2005年 / 11卷
关键词：
maximal itemsets; frequent itemsets; association rules; data mining; backtracking search;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
We present GenMax, a backtrack search based algorithm for mining maximal frequent itemsets. GenMax uses a number of optimizations to prune the search space. It uses a novel technique called progressive focusing to perform maximality checking, and diffset propagation to perform fast frequency computation. Systematic experimental comparison with previous work indicates that different methods have varying strengths and weaknesses based on dataset characteristics. We found GenMax to be a highly efficient method to mine the exact set of maximal patterns.
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页码：223 / 242
页数：19
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