Incremental Mining of Across-streams Sequential Patterns in Multiple Data Streams
學年 99
學期 2
出版（發表）日期 2011-03-01
作品名稱 Incremental Mining of Across-streams Sequential Patterns in Multiple Data Streams
作品名稱（其他語言）
著者 Yang, Shih-yang; Chao, Ching-ming; Chen, Po-zung; Sun, Chu-hao
單位 淡江大學資訊工程學系
出版者 Oulu: Academy Publisher
著錄名稱、卷期、頁數 Journal of Computers 6(3), pp.449-457
摘要 Sequential pattern mining is the mining of data sequences for frequent sequential patterns with time sequence, which has a wide application. Data streams are streams of data that arrive at high speed. Due to the limitation of memory capacity and the need of real-time mining, the results of mining need to be updated in real time. Multiple data streams are the simultaneous arrival of a plurality of data streams, for which a much larger amount of data needs to be processed. Due to the inapplicability of traditional sequential pattern mining techniques, sequential pattern mining in multiple data streams has become an important research issue. Previous research can only handle a single item at a time and hence is incapable of coping with the changing environment of multiple data streams. In this paper, therefore, we propose the IAspam algorithm that not only can handle a set of items at a time but also can incrementally mine across-streams sequential patterns. In the process, stream data are converted into bitmap representation for mining. Experimental results show that the IAspam algorithm is effective in execution time when processing large amounts of stream data.
關鍵字 Multiple data streams; Data stream mining; Sequential pattern mining; Incremental mining
語言 en_US
ISSN 1796-203X
期刊性質 國外
收錄於 EI
產學合作
通訊作者
審稿制度 是
國別 FIN
公開徵稿
出版型式 紙本 電子版
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