The Study of Improved FP-Growth Algorithm in MapReduce

被引：0
|
作者：
Sun, Hong [1 ]
Zhang, Huaxuan [1 ]
Chen, Shiping [2 ]
Hu, Chunyan [2 ]
机构：
[1] Shanghai Univ Sci & Technol, Shanghai Key Lab Modern Opt Syst, Shanghai 201800, Peoples R China
[2] Shanghai Univ Sci & Technol, Shanghai 201800, Peoples R China
来源：
PROCEEDINGS OF THE 1ST INTERNATIONAL WORKSHOP ON CLOUD COMPUTING AND INFORMATION SECURITY (CCIS 2013) | 2013年 / 52卷
基金：
中国国家自然科学基金;
关键词：
FP-Growth; IFP algorithm; MapReduce Introduction;
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：
As FP-Growth algorithm generates a great deal of conditional pattern bases and conditional pattern trees, leading to low efficiency, propose an improved FP-Growth(IFP) algorithm which firstly combine the same patterns based on the situation whether the support of the transaction is greater than the minimum support(min_sup) to mine the frequent patterns. Thus the IFP cuts down on the space and improves the efficiency. It also makes it easy to be paralleled. Further more, combine the IFP algorithm with the MapReduce computing model, named MR-IFP(MapReduce-Improved FP), to improve the capability to deal with the mass data.
引用
收藏
页码：250 / 253
页数：4
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