隨著通訊技術的進步，一些與位置資訊有關的服務也相繼產生。一個可能的應用是找出在空間與時間的上接近的移動群組以便進行市場行銷等商業行為。目前雖然存在著極為精確的定位裝置（誤差在十公尺以下），但是其價格較昂貴且通常是為特殊用途。追蹤一般使用者的定位裝置有著不同程度的誤差，這些的誤差會造成在找尋移動群組的精確度上大大的降低；另外，一些自然的因素，例如溫度、壓力，會影響定位裝置所傳回的位置資訊的正確性，嚴重的話會產造成位置的資訊無法傳回，於是產生了「 無法追蹤的資料」。本論文延伸之前的研究，採用卡門濾波器來對位置(三維資料)資訊進行修正，並增加了將位置資訊轉成兩兩的距離資料(一維)的來進行修正，不僅驗證量測誤差對發掘移動群組的影響，且提出了在不同情境下對這二種類型資料進行修正的策略。我們最後並利用合成資料來評估我們所提出的方法，並歸納出了在不同情境下的相對應用策略。

As the advances of communication techniques, some services related to location information came into existence successively. On such application is on finding out the mobile groups that exhibit spatial and temporal proximities called mobile group mining. Although there exists positioning devices that are capable of achieving a high accuracy with low measurement error. Many consumer-grades, inexpensive positioning devices that incurred various extent of higher measurement error are much more popular. In addition, some natural factors such as temperature, humidity, and pressure may have influences on the precision of position measurement. Worse, moving objects may sometimes become untraceable voluntarily or involuntarily. In this thesis, we extend the previous work on mobile group mining and adopt Kalman filter to correct the noisy data and predict the missing data. Several methods based on Kalman filter that correct/predict either correction data or pair-wise distance data. These methods have been evaluated using synthetic data generated using IBM City Simulator. We identify the operating regions in which each method has the best performance.

Chapter 1 Introduction
1.1 Background
1.2 Motivation
1.3 Organization of this thesis
Chapter 2 Literature Review
2.1Group Pattern Mining
2.2 Kalman Filter
Chapter 3 Location-based Data Correction and Mobile Group Mining
3.1 Mobile Group Mining on Imprecise Location Data
3.1.1 Location Data Corrected by Kalman Filter
3.1.2 Mobile Group Mining.
3.2 Mobile Group Mining on Imprecise and Missing Location Data
3.2.1 Filling up Missing Data by Applying Kalman Filter
3.2.2 Filling up Missing Data Using a Straight-line
3.2.3 Measurement-Proportional
Chapter 4 Distance-based Data Correction and Mobile Group Mining
4.1 Distance-based Mobile Group Mining on Imprecise Data
4.1.1 Pruning Invalid Object Pairs
4.1.2 Distance Data Corrected by Kalman Filter
4.2 Distance-based Mobile Group Mining on Imprecise and Missing Data
4.2.1 Filling Up Missing Distances by Applying Kalman Filter
4.2.2 Filling Up Missing Distances Using A Straight-line
4.2.3 Distance-Proportional
Chapter 5 Performance Evaluation
5.1 Experimental Settings
5.1.1 Synthetic Data Generation
5.1.2 The Performance Metrics
5.2 Experimental Results on Imprecise Data
5.2.2 Execution Time
5.2.3 Quality
5.2.4 Summary
5.3 Experiment Results on Imprecise and Missing Data
5.3.1 Parameter Settings
5.3.2 Experimental Result
5.3.3 Summary
Chapter 6 Conclusions
6.1 Summary
6.2 Future work
References

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