期刊論文
| 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 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/78595 ) |