Incremental Mining of Closed Sequential Patterns in Multiple Data Streams
學年 99
學期 2
出版（發表）日期 2011-05-01
作品名稱 Incremental Mining of Closed 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 Networks 6(5), pp.728-735
摘要 Sequential pattern mining searches for the relative sequence of events, allowing users to make predictions on discovered sequential patterns. Due to drastically advanced information technology over recent years, data have rapidly changed, growth in data amount has exploded and real-time demand is increasing, leading to the data stream environment. Data in this environment cannot be fully stored and ineptitude in traditional mining techniques has led to the emergence of data stream mining technology. Multiple data streams are a branch of the data stream environment. The MILE algorithm cannot preserve previously mined sequential patterns when new data are entered because of the concept of one-time fashion mining. To address this problem, we propose the ICspan algorithm to continue mining sequential patterns through an incremental approach and to acquire a more accurate mining result. In addition, due to the algorithm constraint in closed sequential patterns mining, the generation and records for sequential patterns will be reduced, leading to a decrease of memory usage and to an effective increase of execution efficiency.
關鍵字 Multiple Data Streams; Data Stream Mining; Sequential Pattern Mining; Incremental Mining
語言 en_US
ISSN 1796-2056
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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