近幾年來，隨著行動設備的普及，有越來越多的地理資訊應用軟體被開發出來。因此，各式各樣的物件地理資訊被廣泛的提供。根據物件的移動位置是否接近而將其判別是否為物件群組，是一個新興的研究。現存判別行動群組的方法是以移動物件每一段固定時間所記錄的地理位置為底層資料。然而，移動物件經常會出於自願性的或非自願性的斷線，因此這些每一段固定時間記錄並非一直的適用。在本篇的研究中，我們改以跡軌來描述移動物件的位置，而非固定時間記錄的地理位置。基於這個新模式，我們必須重新定義行動群組問題與發展出有效率的演算法以找出行動群組。並使用IBM City Simulator產生的資料來對所提出的演算法做評估。

In recent years, with the popularization of the mobile devices, more and more location-based applications have been developed. As a result, location data of various objects is widely available. Identifying object groups that tend to move together is an emerging research topic. Existing approaches for identifying mobile group patterns assume the existence of raw location data which records a given object’s position at every equal-spaced time point. However, a moving object may become disconnected voluntarily or involuntarily from time to time, and thus this assumption may not always valid. In this research, we describe the locations of moving object as a (non-continuous) trajectory function. Based on the new model, we re-define the mobile group mining problem and develop efficient algorithms for mining mobile groups. The proposed algorithms are evaluated via synthetic data generated by IBM City Simulator.

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 AGP: Algorithm based on Apriori Property 5
2.1.2 VG-Growth: An Algorithm based on Valid Group Graph Data Structures 6
2.1.3 Evaluation of AGP and VG-growth 8
2.2 Moving object 9
2.2.1 Data Model 9
2.2.2 Query Operations 12
2.2.3 Index 14
Chapter 3 Problem Description 16
3.1 User data model 16
3.2 The group pattern mining problem 18
Chapter 4 Our Approach 21
4.1 Calculate distance between two users 21
4.2 Mining valid group 23
4.3 Algorithm based on Apriori Property 25
4.3.1 Apriori Property 25
4.3.2 ATGP Algorithm 28
4.3.3 TVG Graph and TVG-Growth Algorithm 29
Chapter 5 Performance Evaluation 35
5.1 Synthetic Dataset Generator 35
5.1.1 IBM city simulator 35
5.1.2 Translation to trajectory dataset 37
5.2 Performance of ATGP and TVG-growth 39
5.2.1 Parameter settings 39
5.2.2 Result 40
5.3 Experiments with untraceable data 43
5.3.1 Parameter settings 43
5.3.2 Result 45
Chapter 6 Conclusion 50

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