先前移動物件行動群組探勘的研究，使我們對於如何依照物件移動資料，去判別是否為物件群組有了認知。由於尖端的行動設備，移動資料已可被廣泛取得。然而，現有行動群組探勘的方法並沒有考慮時間層面，因此無法產出更有應用價值的資訊。考量人類行為常常遵循某些時間特性，像是定期的活動、假日、和某些重要日子等，本研究於是致力於發掘遵循某個時間型態而聚集的移動群組。在我們的研究中，我們採用了時間曆表示機制作為我們在時間層面的表示法。基於這個新的考量，我們提出一個新的問題，稱之為時間曆為基礎的移動物件群組探勘問題，並對此問題發展出有效率的演算法。我們也利用合成資料來評估這些提出的演算法。

Previous work on moving object mobile group pattern mining defined and proposed algorithms for mobile group mining based on their individual movement data. Movement data is expected to be widely available owing to the increasing popularity of tractable mobile devices on the cutting edge. However, existing approaches of mobile group pattern mining do not consider temporal dimension. Considering that human beings often act as a group according to some temporal features such as routine activities, in this thesis, we engage in the discovery of valid mobile groups that pertain to the some temporal patterns. In our research, we introduce the calendar-based representation mechanism to be our representation of temporal dimension. Taking the calendar patterns into account, we define a new problem called calendar-based mobile group mining problem and develop efficient algorithms for the problem. The proposed algorithms are evaluated via synthetic location data generated by a sensible data generator.

CHAPTER 1 INTRODUCTION 1
1.1 Background 1
1.2 Motivation 2
1.3 Thesis Outline 3
CHAPTER 2 LITERATURE REVIEW 4
2.1. Group Pattern Mining 4
2.1.1 Apriori

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