近年來，由於適地性服務的概念廣泛的應用，因此引發出許多相關的研究，其中，一個重要的研究議題是預測使用者的行為。在本論文中，我們提出一個新的資料探勘的演算法TMSP-Mine，針對在適地性服務的環境下的使用者，有效率的找出具有時間特性之循序移動樣式。在研究中，我們所提出的方法是第一個同時考慮移動路徑與時間區段的循序樣式。此外，利用TMSP-Mine找出的樣式，可以用來預測使用者接下來會到達的位置以及使用者的行為，以提供使用者更好的服務。最後，我們做的一系列的實驗，評估我們的方法在不同的參數下的執行的效率。

In recent years, a number of studies have been done on Location-Based Service (LBS) due to the wide applications. One important research issue is the tracking and prediction of users’ mobile behavior. In this paper, we propose a novel data mining algorithm named TMSP-Mine for efficiently discovering the Temporal Mobile Sequential Patterns (TMSPs) of users in LBS environments. To our best knowledge, this is the first work on mining the mobile sequential patterns associated with moving paths and time intervals in LBS environments. Furthermore, we propose novel location prediction strategies that utilize the discovered TMSPs to effectively predict the next movement of mobile users. Finally, we conducted a series of experiments to evaluate the performance of the proposed method under different system conditions by varying various parameters.

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