A Novel Frequent Pattern Mining Algorithm for Real-time Radar Data Stream

被引：2

作者：

Huang, Fang ^{[1
]}

Zheng, Ningning ^{[1
]}

机构：

[1] Shandong Agr & Engn Univ, Informat Sci & Engn Coll, Jinan 250100, Shandong, Peoples R China

来源：

TRAITEMENT DU SIGNAL | 2019年 / 36卷 / 01期

关键词：

frequent pattern; data mining; radar data; data stream; index pattern tree (IPT); SLIDING WINDOW; ITEMSETS;

D O I：

10.18280/ts.360103

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper attempts to improve the radar data quality and discover intrusion behaviors by mining frequent patterns of real-time data. For this purpose, the author improved the frequent pattern mining algorithm for static datasets, and proposed a maximum frequent pattern mining algorithm based on bitmap mapping. Next, a new frequent pattern mining algorithm was designed specifically for data stream with the storage structure of index pattern tree (IPT). The new algorithm was applied to process the real-time radar data stream. The application results demonstrate efficiency of our algorithm in time and space.

引用

页码：23 / 30

页数：8

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