TY - CHAP
T1 - Mining weighted partial periodic patterns
AU - Yang, Kung Jiuan
AU - Hong, Tzung Pei
AU - Chen, Yuh Min
AU - Lan, Guo Cheng
N1 - Publisher Copyright:
© 2013, Springer Science+Business Media Dordrecht 2013.
PY - 2013
Y1 - 2013
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.
UR - http://www.scopus.com/inward/record.url?scp=85059220354&partnerID=8YFLogxK
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 -