Mining both frequent and rare episodes in multiple data streams

ISCAS OpenIR

	Mining both frequent and rare episodes in multiple data streams
	Hu, Zhongyi (1); Liu, Wei (1); Wang, Hongan (1)
	2013
会议名称	2013 10th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2013
页码	753-761
会议日期	July 23, 2013 - July 25, 2013
会议地点	Shenyang, China
收录类别	CPCI ; EI
出版地	IEEE Computer Society
ISBN	9781467352536
部门归属	(1) Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China
摘要	In this paper, we describe a method for mining both frequent episodes and rare episodes in multiple data streams. The main issues include episodes mining and data streams relationship processing. Therefore, a mining algorithm together with two dedicated handling mechanisms is presented. We propose the concept of alternative support for discovering frequent and rare episodes, and define the semantic similarity of event sequences for analyzing the relationships between data streams. The algorithm extracts basic episode information from each data stream and keeps the information in episode sets. Then analyze relationships of episode sets and merge similar episode sets, and mining episode rules from the merged sets by alternative support and confidence. From experiments, we find that our mining algorithm is successful for processing multiple data streams and mining frequent and rare episodes. Our research results may lead to a feasible solution for frequent and rare episodes mining in multiple data streams. © 2013 IEEE.; In this paper, we describe a method for mining both frequent episodes and rare episodes in multiple data streams. The main issues include episodes mining and data streams relationship processing. Therefore, a mining algorithm together with two dedicated handling mechanisms is presented. We propose the concept of alternative support for discovering frequent and rare episodes, and define the semantic similarity of event sequences for analyzing the relationships between data streams. The algorithm extracts basic episode information from each data stream and keeps the information in episode sets. Then analyze relationships of episode sets and merge similar episode sets, and mining episode rules from the merged sets by alternative support and confidence. From experiments, we find that our mining algorithm is successful for processing multiple data streams and mining frequent and rare episodes. Our research results may lead to a feasible solution for frequent and rare episodes mining in multiple data streams. © 2013 IEEE.
关键词	Data Stream Mining Episode Mining Rare Episode Frequent Episode Multiple Data Streams
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16530
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Hu, Zhongyi ,Liu, Wei ,Wang, Hongan . Mining both frequent and rare episodes in multiple data streams[C]. IEEE Computer Society,2013:753-761.