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研究生:李健民
研究生(外文):Chien-Ming Lee
論文名稱:從空間時間資料庫挖掘移動群組
論文名稱(外文):Discovering Moving Clusters from Spatial-Temporal Databases
指導教授:黃三益黃三益引用關係
指導教授(外文):San-Yih Hwang
學位類別:碩士
校院名稱:國立中山大學
系所名稱:資訊管理學系研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:英文
論文頁數:63
中文關鍵詞:空間時間資料庫移動群組
外文關鍵詞:Spatial-Temporal DatabasesMoving Clusters
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隨著電腦與通訊技術的進步,分析移動物件上的群集關係,近幾年逐漸吸引到各方的注意力。一個令人感到興趣的問題是找出移動群組,而移動群組裡的物件成員都必需在一起移動一段足夠長的時間。然而,一個移動群組因為每一個物件的目的地會有所不同,所以在一段時間之後,一個移動群組易於分裂拆散。為了能夠鑑別出一些移動群組,我們藉由明確的參數設定,進而定義正規的移動群組。接著我們以移動群組的定義為基礎,提出精確的方法來為這些移動物件做分群。而我們提出的方法將在兩種類型的資料情況下進行評估,分別為合成資料、潛在已知的資料。最後,我們藉由一整個實驗的評估與比較來驗證我們提出的方法。
Owing to the advances of computer and communication technologies, clustering analysis on moving objects has attracted increasing attention in recent years. An interesting problem is to find the moving clusters composed of objects which move along for a sufficiently long period of time. However, a moving cluster inclines to break after some time because of the goal change in each individual object. In order to identify the set of moving clusters, we propose the formal definition of moving clusters with semantically clear parameters. Based on the definition, we propose delicate approaches to cluster moving objects. The proposed approaches are evaluated using data generated with and without underlying model. We validate our approaches with a through experimental evaluation and comparison.
CHAPTER 1 - Introduction 1
1.1 Background 1
1.2 Motivation 1
1.3 Organization of this thesis 3
CHAPTER 2 - Literature Review 4
2.1 Mobile Group Pattern Mining 4
2.2 Static Spatial Data Clustering 5
2.2.1 Partitioning Methods 6
2.2.2 Hierarchical Methods 6
2.2.3 Density-based Methods 7
2.2.4 Grid-based Methods 7
2.2.5 Evaluations of Spatial Clustering Methods 7
2.3 Spatial-Temporal Data Clustering 8
2.3.1 ST-DBSCAN 8
2.4 Moving Objects Clustering 9
2.4.1 Moving Objects Trajectories 9
2.4.2 Moving Clusters 10
2.4.3 Moving Micro-clusters 11
2.5 Moving Objects and Queries Clustering 13
2.5.1 Motion Model 13
2.5.2 The SCUBA Algorithm 14
CHAPTER 3 - Problem Statement 16
3.1 Object Movement Database 16
3.2 Cluster Validity 17
CHAPTER 4 - Our Approach 21
4.1 Time-point-clustering 21
4.2 Time-window-clustering 23
CHAPTER 5 - Performance Evaluation 28
5.1 Data Generation with a Underlying Model 28
5.2 Data Generation without Underlying Model 29
5.2.1 IBM City Simulator 29
5.2.2 Translation to Trajectory Data 30
5.3 Experimental Results on Data with Underlying Model 31
5.3.1 Parameter Settings 32
5.3.2 Quality 32
5.3.3 Summary 38
5.4 Experimental Results on Synthetic Data 38
5.4.1 Parameter Settings 38
5.4.2 Execution Time 38
5.4.3 Quality 43
5.4.4 Summary 47
CHAPTER 6 - Conclusions 49
6.1 Summary 49
6.2 Future Work 49
References 50
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