雙屬性集合之空間分群演算法-應用於地理資料

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曾心怡(Hsin-I Tseng) 查詢紙本館藏

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資訊管理學系

論文名稱

雙屬性集合之空間分群演算法-應用於地理資料
(A Two-Attributes-Set Spatial Clustering Algorithm for Geographic Data)

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摘要(中)

分群是資料探勘技術中的一個重要研究領域，且已在許多領域中被廣泛的研究與應用。分群是在空間上將相似的資料劃分成同一群並以少數的群集代替龐大的資料。然而目前傳統演算法針對地理資料上的空間分群皆為單一屬性，但實際上是可以將地理資料屬性做分類後加以分群，例如氣象局想了解不同地區的觀測站所測量氣候現象是否相似，故針對經度、緯度、溫度、雨量、溼度等屬性進行分群，我們可以察覺當中包含兩種屬性，經度和緯度的屬性為空間上的屬性，溫度、雨量和濕度等為氣象特徵上的屬性。傳統演算法在分群時，空間屬性與特徵屬性必須是相同的，因此無法針對此類問題產生良好的分群結果。因此我們提出雙屬性空間分群演算法，可以兼顧空間與多維特徵屬性分群，達到可以以空間上的屬性來分出每一群群內屬性相似，群間不相似的分群結果。

摘要(英)

Cluster analysis has recently become a highly active topic in data mining research. However, traditional clustering algorithms had a restriction that they consider only one set of attributes. Actually, we can divide all attributes of a spatial object into two attribute sets. For example, Weather Bureau would like to know which regions have similar climate phenomenon, where each weather station are described by latitude and longitude attributes, and measurement of temperature, precipitation attributes. Therefore, two different attribute sets are required for spatial clustering, where one set is spatial attributes and the other one is characteristic attributes. Traditional algorithms do not distinguish the two sets of attributes, which lead to low quality spatial clustering results. We propose Two-Attributes-Set Spatial Clustering, generating clusters that can be segmented by characteristic attributes and objects in the same cluster are similar in spatial attributes as well.

關鍵字(中)

★ 資料挖掘
★ 空間分群

關鍵字(英)

★ Data mining
★ Cluster analysis
★ Spatial Clustering

論文目次

Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Research Objectives 5
1.4 Thesis Framework 5
Chapter 2 Related Works 6
2.1 Cluster spatial objects without characteristic attributes 7
2.2 Cluster spatial objects with characteristic attributes 9
Chapter 3 The Problem and the Definitions 13
3.1 Research Problem 13
3.2 Definitions 14
Chapter 4 Two-Attributes-Set Spatial Clustering 24
4.1 Overview of the Algorithm 24
4.2 The Clustering Algorithm 27
4.2.1 Parameters of the Algorithm 27
4.2.2 Procedure of the Algorithm 28
4.2.3 Algorithm 29
4.3 Example 33
Chapter 5 Experiments 39
5.1 Content of Experiments 39
5.2 Performance Evaluation 40
5.2.1 Indicators for clustering results measures 40
5.2.2 Parameter Optimization 43
5.2.3 Clustering results measures 49
5.2.4 Draw the Clustering results 53
Chapter 6 Conclusions 59
6.1 Implications for Academic Researches 59
6.2 Implications for Business Practitioners 60
6.3 Future Works 60
References 61

參考文獻

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指導教授

陳彥良(Yen-Liang Chen)

審核日期

2010-7-14

推文