TY - GEN
T1 - Discovering valuable user behavior patterns in mobile commerce environments
AU - Shie, Bai En
AU - Hsiao, Hui Fang
AU - Yu, Philip S.
AU - Tseng, S.
PY - 2012
Y1 - 2012
N2 - Mining user behavior patterns in mobile environments is an emerging topic in data mining fields with wide applications. By integrating moving paths with purchasing transactions, one can find the sequential purchasing patterns with the moving paths, which are called mobile sequential patterns of the mobile users. Mobile sequential patterns can be applied not only for planning mobile commerce environments but also analyzing and managing online shopping websites. However, unit profits and purchased numbers of the items are not considered in traditional framework of mobile sequential pattern mining. Thus, the patterns with high utility (i.e., profit here) cannot be found. In view of this, we aim at integrating mobile data mining with utility mining for finding high utility mobile sequential patterns in this study. A novel algorithm called UMSP L (high Utility Mobile Sequential Pattern mining by a Level-wised method) is proposed to efficiently find high utility mobile sequential patterns. The experimental results show that the proposed algorithm has excellent performance under various system conditions.
AB - Mining user behavior patterns in mobile environments is an emerging topic in data mining fields with wide applications. By integrating moving paths with purchasing transactions, one can find the sequential purchasing patterns with the moving paths, which are called mobile sequential patterns of the mobile users. Mobile sequential patterns can be applied not only for planning mobile commerce environments but also analyzing and managing online shopping websites. However, unit profits and purchased numbers of the items are not considered in traditional framework of mobile sequential pattern mining. Thus, the patterns with high utility (i.e., profit here) cannot be found. In view of this, we aim at integrating mobile data mining with utility mining for finding high utility mobile sequential patterns in this study. A novel algorithm called UMSP L (high Utility Mobile Sequential Pattern mining by a Level-wised method) is proposed to efficiently find high utility mobile sequential patterns. The experimental results show that the proposed algorithm has excellent performance under various system conditions.
KW - Utility mining
KW - high utility mobile sequential pattern
KW - mobile environments
KW - mobility pattern mining
UR - http://www.scopus.com/inward/record.url?scp=84857714606&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-28320-8_7
DO - 10.1007/978-3-642-28320-8_7
M3 - Conference contribution
AN - SCOPUS:84857714606
SN - 9783642283192
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 77
EP - 88
BT - New Frontiers in Applied Data Mining - PAKDD 2011 International Workshops, Revised Selected Papers
T2 - 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011
Y2 - 24 May 2011 through 27 May 2011
ER -