| Incrementally Mining Temporal Patterns in Interval-based Databases | |
|---|---|
| 學年 | 103 |
| 學期 | 1 |
| 發表日期 | 2014-10-30 |
| 作品名稱 | Incrementally Mining Temporal Patterns in Interval-based Databases |
| 作品名稱（其他語言） | |
| 著者 | Chen, Yi-Cheng; Weng, Julia Tzu-Ya; Wang, Jun-Zhe; Chou, Chien-Li; Huang, Jiun-Long; Lee, Suh-Yin |
| 作品所屬單位 | 資訊工程學系暨研究所 |
| 出版者 | IEEE |
| 會議名稱 | The 2014 International Conference on Data Science and Advanced Analytics (DSAA 2014) |
| 會議地點 | Shanhai, China |
| 摘要 | In several applications, sequence databases generally update incrementally with time. Obviously, it is impractical and inefficient to re-mine sequential patterns from scratch every time a number of new sequences are added into the database. Some recent studies have focused on mining sequential patterns in an incremental manner; however, most of them only considered patterns extracted from time point-based data. In this paper, we proposed an efficient algorithm, Inc_TPMiner, to incrementally mine sequential patterns from interval-based data. We also employ some optimization techniques to reduce the search space effectively. The experimental results indicate that Inc_TPMiner is efficient in execution time and possesses scalability. Finally, we show the practicability of incremental mining of interval-based sequential patterns on real datasets. |
| 關鍵字 | dynamic representation; incremental mining; interval-based pattern; sequential pattern mining |
| 語言 | en_US |
| 收錄於 | |
| 會議性質 | 國際 |
| 校內研討會地點 | 無 |
| 研討會時間 | 20141030~20141101 |
| 通訊作者 | Chen, Yi-Cheng |
| 國別 | CHN |
| 公開徵稿 | Y |
| 出版型式 | 電子版 |
| 出處 | The 2014 International Conference on Data Science and Advanced Analytics |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/99421 ) |
GoogleAnalytics