A Comparative Analysis of Frequent Pattern Mining Algorithms Used for Streaming Data

被引：0
|
作者：
Shalini [1 ]
Jain, Sanjay Kumar [1 ]
机构：
[1] Natl Inst Technol, Dept Comp Engn, Kurukshetra 136119, Haryana, India
来源：
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND AUTOMATION (ICCCA) | 2017年
关键词：
Data streams; Stream data mining; Frequent pattern mining;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Frequent pattern mining across streaming data is a challenging task. It require real time response and incurs great computational complexity. In this paper, we discuss challenges of developing frequent pattern mining algorithms for streaming data, compare three algorithms proposed in literature and explore scope of improvement in the algorithms. We discuss the suitability of these algorithms according to applications.
引用
收藏
页码：250 / 255
页数：6
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