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研究生:蕭清泉
研究生(外文):Ching-Chuan Hsiao
論文名稱:應用序列樣式探勘於導覽系統之開發
論文名稱(外文):Developing a Guiding System Based on Sequential Pattern Mining
指導教授:蔡介元蔡介元引用關係
學位類別:碩士
校院名稱:元智大學
系所名稱:工業工程與管理學系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:97
語文別:英文
論文頁數:84
中文關鍵詞:I-PrefixSpan演算法時間間隔序列型樣導覽系統
外文關鍵詞:I-PrefixSpan algorithmTime-interval sequential patternsGuiding system
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導覽服務對於參訪博物館的遊客而言是很重要的;假使博物館沒有提供導覽服務,遊客將會花很多時間在尋找想要參觀的展覽品或者甚至會在博物館裡迷路。所以如何發展一個滿足遊客需求及客製化服務的導覽系統就變成一個重要的議題。因此,本研究提出一個博物館推薦路徑系統來產生符合遊客需求的路徑。首先將所有參訪路徑依照遊客的個人屬性分類,接著本系統應用I-PrefixSpan演算法在不同個人屬性的子路徑資料庫中,分別進行時間間隔序列型樣的探勘。遊客於PDA輸入他們的個人屬性及預計參觀時間後,本系統將依照使用者在PDA上輸入的限制條件,進行時間間隔序列型樣的篩選。因為候選參訪路徑的數量可能眾多。因此,本系統會依據每一條候選參訪路線的所跨越的展覽區域個數、所含有的展覽品個數及預計參觀時間接近程度相互比較,進而在眾多的候選參訪路徑進行評分及排序。最後,若有多數個符合遊客限制條件的推薦路徑,則會經由本研究所提出的評估方法來進行排序,並且將排序前三名的路線顯示回遊客的PDA上。
Guiding service plays an important role for visitors to visit museum. Without guiding service, visitors might spend much time for finding exhibits or get lost in the museums. Therefore, how to develop a guiding system to satisfy visitors’ requirements becomes an important issue for museums. This research proposes a museum touring path suggestion system to derive the touring path suggestions that satisfy visitors’ requirements. First, all visiting paths are classified to different sub-database according to visitors’ personal profile. The I-PrefixSpan algorithm is applied to discover time-interval sequential patterns in different personal profile sub-databases. After visitors submit their personal profiles and intended visiting time on PDA, the system will search the candidate touring paths which are filtered out according to visitor’s requirements. Because the number of candidate touring paths might be huge, this system will rank these paths according to the path section count, path length, and time closeness of each candidate touring path. Finally, the candidate touring paths are prioritized and the first three priority paths are sent back to visitor’s PDA.
ABSTRACT i
摘要 ii
Table of Content iii
List of Figure v
List of Table vi
Chapter 1 Introduction 1
1.1 Research Background 1
1.2 Research Problem 2
1.3 Research Objective 3
1.4 Thesis Organization 3
Chapter 2 Literature Review 5
2.1 Data Mining 5
2.2 Sequential Pattern Mining 7
2.2.1 The AprioriAll Algorithm 8
2.3 The Related Researches in time-interval Sequential Patterns 12
2.4 Radio Frequency Identification (RFID) 14
2.4.1 The application of RFID 18
Chapter 3 A Touring Path Suggestion System 20
3.1 The Guiding System Architecture 20
3.2 Frequent Time-Interval Path Mining Procedure 24
3.2.1 Data Preprocess: Based on Visitors’ Personal Profile 24
3.2.2 The Frequent Time-Interval Path Mining 26
3.2.3 The Total Visiting Time Calculation 31
3.2.4 An Example in Frequent Time-interval Path Mining Procedure 31
3.3 The Path Generating Mechanism 34
3.3.1 The candidate touring paths 35
3.3.2 The Definitions of Path Section Count, Path Length, and Time Closeness 36
3.3.3 Suggestion Priority 37
3.3.4 An Example in Path Generating Mechanism 40
Chapter 4 Implementation and Experiment Results 45
4.1 Case Description 45
4.1.1 Exhibition Layout 45
4.1.2 Data Source 47
4.1.3 Frequent Time-interval Path Mining Procedure 51
4.1.4 The Candidate Touring Paths 52
4.1.5 Suggestion Priority 55
4.2 Parameter Analysis 60
4.2.1 Discussion of the Time Interval Range 60
4.2.2 Discussion of 61
4.2.3 Discussion of , , and 65
4.2.4 Discussion of Different Personal Profile 70
4.2.5 Discussion of Intended Visiting Time Range 74
Chapter 5 Conclusions and Future Researches 78
5.1 Conclusions 78
5.2 Future Researches 79
Reference 81
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