摘要
In the data mining area, partial periodic pattern mining has become an important issue in many business applications. Although weighted sequential pattern mining algorithms have been widely discussed, until now, there is no further discussion in the field of weighed partial periodic pattern mining. Thus, this work introduces a new research issue, named weighted partial periodic pattern mining, which considers the individual significances of events in an event sequence. In addition, a projection-based mining algorithm is presented to effectively handle the weighted partial periodic pattern mining problem. The experimental results show the proposed algorithm is efficient.
| 原文 | English |
|---|---|
| 主出版物標題 | Springer Proceedings in Complexity |
| 發行者 | Springer |
| 頁面 | 47-54 |
| 頁數 | 8 |
| DOIs | |
| 出版狀態 | Published - 2013 一月 1 |
出版系列
| 名字 | Springer Proceedings in Complexity |
|---|---|
| ISSN（列印） | 2213-8684 |
| ISSN（電子） | 2213-8692 |
指紋
All Science Journal Classification (ASJC) codes
- Applied Mathematics
- Modelling and Simulation
- Computer Science Applications
引用此文
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Mining weighted partial periodic patterns. / Yang, Kung Jiuan; Hong, Tzung Pei; Chen, Yuh Min; Lan, Guo Cheng.
Springer Proceedings in Complexity. Springer, 2013. p. 47-54 (Springer Proceedings in Complexity).研究成果: Chapter
TY - CHAP
T1 - Mining weighted partial periodic patterns
AU - Yang, Kung Jiuan
AU - Hong, Tzung Pei
AU - Chen, Yuh Min
AU - Lan, Guo Cheng
PY - 2013/1/1
Y1 - 2013/1/1
N2 - In the data mining area, partial periodic pattern mining has become an important issue in many business applications. Although weighted sequential pattern mining algorithms have been widely discussed, until now, there is no further discussion in the field of weighed partial periodic pattern mining. Thus, this work introduces a new research issue, named weighted partial periodic pattern mining, which considers the individual significances of events in an event sequence. In addition, a projection-based mining algorithm is presented to effectively handle the weighted partial periodic pattern mining problem. The experimental results show the proposed algorithm is efficient.
AB - In the data mining area, partial periodic pattern mining has become an important issue in many business applications. Although weighted sequential pattern mining algorithms have been widely discussed, until now, there is no further discussion in the field of weighed partial periodic pattern mining. Thus, this work introduces a new research issue, named weighted partial periodic pattern mining, which considers the individual significances of events in an event sequence. In addition, a projection-based mining algorithm is presented to effectively handle the weighted partial periodic pattern mining problem. The experimental results show the proposed algorithm is efficient.
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UR - http://www.scopus.com/inward/citedby.url?scp=85059220354&partnerID=8YFLogxK
U2 - 10.1007/978-94-007-7293-9_6
DO - 10.1007/978-94-007-7293-9_6
M3 - Chapter
AN - SCOPUS:85059220354
T3 - Springer Proceedings in Complexity
SP - 47
EP - 54
BT - Springer Proceedings in Complexity
PB - Springer
ER -