Efficiently mining maximal frequent itemsets based on digraph

被引：0
|
作者：
Ren, Zhibo [1 ]
Zhang, Qiang [2 ]
Ma, Xiujuan [3 ]
机构：
[1] Hebei Univ, Sch Management, Baoding, Peoples R China
[2] Beijing Inst Technol, Sch Management & Econ, Beijing, Peoples R China
[3] North China Elect Power Univ, Baoding, Peoples R China
来源：
FOURTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 2, PROCEEDINGS | 2007年
关键词：
D O I：
10.1109/FSKD.2007.268
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 MFIMiner, a new algorithm for mining maximal frequent itemsets. The algorithm has a preprocessing phase in which a digraph is constructed The digraph represents the set of the frequent 2-itemsets which is the key issue of the performance of the data mining. Then the search for maximal frequent itemsets is done in the digraph. Experiments show that the algorithm is efficient not only to dense data, but to sparse data.
引用
收藏
页码：140 / +
页数：2
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