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博碩士論文 etd-0721105-165723 詳細資訊
Title page for etd-0721105-165723
論文名稱
Title
從不確定性資料庫裡探勘移動群組型態
Mining Mobile Groups from Uncertain Location Databases
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
51
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2005-07-19
繳交日期
Date of Submission
2005-07-21
關鍵字
Keywords
Smooth、Kalman Filter、行動群組
Kalman Filter, Mobile group mining, Smooth
統計
Statistics
本論文已被瀏覽 5920 次,被下載 19
The thesis/dissertation has been browsed 5920 times, has been downloaded 19 times.
中文摘要
隨著行動通訊裝置的普及,要取得不同物件的地理位置資料比以往更加方便與容易。也因為如此,藉由辨識在空間或時間上接近的移動群組以便進行商業行銷、犯罪偵測或者是學術性研究的應用也越來越普遍。雖然目前最先進的定位儀器可以將量測誤差降低至十公尺以下,但也只限定於特殊用途並且收費昂貴,例如:軍事科技。在我們一般用途與日常使用的定位儀器,其誤差可能由十到一百公尺不等。這樣的誤差相對來說可能致使我們在發掘群組的精確度上產生懷疑。也因為如此,本篇論文驗證了由量測誤差所造成對於辨識移動群組正確性的影響,並採用Kalman Filter 和 RTS smoothing 的修正方式,企圖將由量測所產生的誤差降至最低,以提高發掘群組資訊的精確度與可信度。在大部分的情況下,修正後的資料能產生更多正確的移動群組,然而,當量測誤差很小且物體移動的速度較緩和時,由量測的資料直接產生的移動群組反而能得到較佳的結果。
Abstract
As the mobile communication devices become popular, getting the location data of various objects is more convenient than before. Mobile groups that exhibit spatial and temporal proximities can be used for marketing, criminal detection, and ecological studies, just to name a few. Although nowadays the most advanced position equipments are capable of achieving a high accuracy with the measurement error less than 10 meters, they are still expensive. Positioning equipments using different technologies incur different amount of measurement errors ranging from 10 meters to a few hundred meters. In this thesis, we examine the impact of measurement errors on the accuracy of identified valid mobile groups and apply Kalman Filter and RTS smoothing as the one-way and two-way correction to correct the measurement data. In most settings, the corrected location data yield more accurate valid mobile groups. However, when the measurement error is small and users do not make abrupt change in their speed, mining mobile groups directly on the measurement data, however, yield better results.
目次 Table of Contents
CHAPTER 1. INTRODUCTION 1
1.1 BACKGROUND 1
1.2 MOTIVATION 2
1.3 ORGANIZATION OF THIS THESIS 3
CHAPTER 2. LITERATURE REVIEW 5
2.1 GROUP PATTERN MINING 5
2.2 TRAJECTORY-BASED MINING GROUP PATTERN 8
2.3 KALMAN FILTER 12
2.4 SMOOTHING 17
CHAPTER 3. APPLYING KALMAN FILTER TO GENERATE FIXED-POINT LOCATION DATA 20
3.1 ONE-WAY CORRECTION 20
3.1.1 KALMAN FILTER PARAMETER SETTINGS 20
3.1.2 THE PROCEDURE OF KALMAN FILTER 22
3.2 TWO-WAY CORRECTION 23
CHAPTER 4. MINING GROUP PATTERNS WITH LOCATION CORRECTION 25
4.1 MINING MOBILE GROUP PATTERNS ON LOCATION DATA AT FIXED TIME POINTS 25
4.2 MINING MOBILE GROUP PATTERNS ON LOCATION DATA WITH TRAJECTORIES 26
4.3 SUMMARY 30
CHAPTER 5. PERFORMANCE EVALUATION 32
5.1 SYNTHETIC DATASET GENERATOR 32
5.2 THE EFFECT OF DATA CORRECTION 36
5.2.1 PARAMETER SETTINGS 36
5.2.2 RESULT 37
5.3 ACCURACY OF MINED MOBILE GROUPS 40
5.3.1 PARAMETER SETTINGS 41
5.3.2 EXPERIMENTAL RESULTS 41
5.4 RUNNING TIMES 45
5.4.1 PARAMETER SETTINGS 45
5.4.2 RESULTS 46
CHAPTER 6. CONCLUSIONS 48
REFERENCES 49
參考文獻 References
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