A genetic algorithm based searching of maximal frequent itemsets

被引：0

作者：

Huang, JP ^{[1
]}

Yang, CT ^{[1
]}

Fu, CH ^{[1
]}

机构：

[1] So Taiwan Univ Technol, Tainan, Taiwan

来源：

IC-AI '04 & MLMTA'04 , VOL 1 AND 2, PROCEEDINGS | 2004年

关键词：

genetic algorithm; association rules; maximal frequent itemsets; data mining;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a novel approach to generate maximal frequent itemsets using principles of genetic algorithm. The new approach, we called GAMax is an implementation using genetic algorithm to find the maximal frequent itemsets in transaction database. The objective is to identify maximal frequent itemsets in lexicographic free, needless to enumerate all the frequent itemsets level by level. In natures of GAs, the significant contribution of GAMax is: providing a general algorithm for generating frequent itemsets that is scale independent to the size of database. We also expect GAMax to open new visions in combining these two flourishing domains of research.

引用

页码：548 / 554

页数：7