Frequent Itemset Mining on Hadoop

被引：0
|
作者：
Ferenc Kovacs [1 ]
Illes, Janos [1 ]
机构：
[1] Budapest Univ Technol & Econ, Dept Automat & Appl Informat, Budapest, Hungary
来源：
IEEE 9TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL CYBERNETICS (ICCC 2013) | 2013年
关键词：
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
One of the most important problems in data mining is frequent itemset mining. It requires very large computation and I/O traffic capacity. For that reason several parallel and distributed mining algorithms were developed. Recently the mapreduce distributed data processing paradigm is unavoidable and porting the current algorithms to mapreduce is in focus. In this paper a substantial frequent itemset mining algorithms and their mapreduce implementations are introduced and investigated. An algorithm improvement is also proposed and analyzed.
引用
收藏
页码：241 / 245
页数：5
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