教師資料查詢 | 類別: 期刊論文 | 教師: 陳伯榮 CHEN PO-ZUNG (瀏覽個人網頁)

標題: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
語言英文(美國)
ISSN1796-2056
期刊性質國外
收錄於EI
產學合作
通訊作者
審稿制度
國別芬蘭
公開徵稿
出版型式紙本;電子版
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