A Study of Mining User Behavior in Mobile Environments

• Main Authors: Kun-Lin Lu, 呂昆霖
• Other Authors: Sue-Chen Hsueh
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/75484297816439246674

碩士 === 朝陽科技大學 === 資訊管理系碩士班 === 95 === With the development of wireless technology, mobile users may request services they need anytime anywhere. Utilizing these transaction data effectively is an important issue. Mining sequential pattern and association rule were used in mobile commerce environment afterward. Nevertheless, no research mines multidimensional user behavior considering time constraint thresholds simultaneously, and negative association between the movement of mobile users. First, we propose a new algorithm MDPG (Multidimensional Delimited Pattern Growth) basing on Delisp and mining the mobile user behavior patterns effectively. Delisp is a sequential pattern mining algorithm employs pattern growth technique to improve the performance of GSP. Considering four time constraints and three-dimensional attributes (Space, Service and Time) in MDPG increases the practicality and interest of sequential patterns. Second, although many researches have confirmed that negative association rule is as practical and important as positive association rule, most of them based on Apriori algorithm and mined confined negative association rules. We propose a method GENAR which may discover generalized negative association rules. GENAR outperforms Apriori-based algorithm and is suitable to use in mobile commerce environment. The mined results may be used for immediate marketing advertisement, effective resource allocation, accurate position predicting and improving system development, etc.