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博碩士論文 etd-0801106-122022 詳細資訊
Title page for etd-0801106-122022
論文名稱
Title
以時間曆為基礎的移動群組型態探勘
The Discovery of Calendar-Based Mobile Group Patterns in Spatial-Temporal 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
57
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
2006-07-21
繳交日期
Date of Submission
2006-08-01
關鍵字
Keywords
時間、群組
calendar pattern, group pattern mining, mobile group pattern
統計
Statistics
本論文已被瀏覽 5995 次,被下載 1408
The thesis/dissertation has been browsed 5995 times, has been downloaded 1408 times.
中文摘要
近幾年來,由於行動設備的普及,以及相關設備軟硬體的發展,行動資料已可廣泛取得,根據地理資訊判斷物體是否為同一群組是一個新興的研究。本研究考量在時間與空間兩個面向上,移動群組的發掘。先前的研究將時間視為整段區塊或是簡單的時間循環,但是考慮到一般群組的聚集情況往往遵循某個時間型態,尤其是人類的群組的出現情況,常與約定俗成的時間曆有關,但卻又不一定嚴格遵守特定的規律。所以我們提出一個更具彈性的時間曆表示法(flexible calendar pattern),並針對建構在新的時間面向表示法上的移動群組發掘發展出有效率的演算法。本文以IBM City Simulator產生的合成資料進行演算法的效率評估。實驗結果證明本研究所提出的方法可以有效的降低所需的執行時間。
Abstract
In the past few years, due to the development of the mobile devices and the improvement of database technology, the geometric information has become widely available. Identifying object groups based on spatial-temporal dimension is an emerging research topic. Previous work has incorporating the spatial and temporal information pertaining to moving objects in finding mobile groups. Considering that mobile groups tend to exhibit some calendar-like temporal features, we define a new temporal presentation mechanism called flexible calendar pattern, which allows users to specify the desired calendar patterns at a coarse level. In addition, we developed efficient algorithms for mining mobile groups pertaining some user-specified flexible calendar pattern. The proposed algorithms are evaluated via the synthetic data generated by IBM City Simulator. The results show that our approaches prove to perform more efficiently than other intuitive approaches.
目次 Table of Contents
CHAPTER 1 INTRODUCTION 5
1.1 BACKGROUND 5
1.2 MOTIVATION 6
1.3 THESIS OUTLINE 6
CHAPTER 2 LITERATURE REVIEW 9
2.1 MOBILE GROUP MINING 9
2.2 TEMPORAL ASSOCIATION RULE MINING 10
2.3 CALENDAR-BASED SPATIO-TEMPORAL ASSOCIATION RULE MINING 12
CHAPTER 3 THE PROBLEM 20
3.1 AUXILIARY DEFINITIONS 20
3.2 THE MINING PROBLEM 23
CHAPTER 4 THE MINING ALGORITHMS 26
4.1 CAGP 26
4.2 CVG-GROWTH 33
CHAPTER 5 PERFORMANCE EVALUATION 39
5.1 EXPERIMENTAL DESIGN 39
5.2 EVALUATING CAGP 42
5.3 EVALUATING CVG-GROWTH 46
5.4 COMPARING CAGP AND CVG-GROWTH 49
CHAPTER 6 CONCLUSIONS 52
REFERENCES 53
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