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Title:
Mining both frequent and rare episodes in multiple data streams
Author: Hu, Zhongyi (1) ; Liu, Wei (1) ; Wang, Hongan (1)
Conference Name: 2013 10th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2013
Conference Date: July 23, 2013 - July 25, 2013
Issued Date: 2013
Conference Place: Shenyang, China
Keyword: data stream mining ; episode mining ; rare episode ; frequent episode ; multiple data streams
Publish Place: IEEE Computer Society
Indexed Type: CPCI ; EI
ISBN: 9781467352536
Department: (1) Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China
Abstract: 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.
English Abstract: 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.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16530
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Hu, Zhongyi ,Liu, Wei ,Wang, Hongan . Mining both frequent and rare episodes in multiple data streams[C]. 见:2013 10th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2013. Shenyang, China. July 23, 2013 - July 25, 2013.
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