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系統識別號 U0026-2608201302205100
論文名稱(中文) 異質性資料之知識探索機制-以植物栽培提升銀髮族樂活為例
論文名稱(英文) Knowledge Discovery Mechanism of Heterogeneous Data: Enhance Elderly LOHAS via Planting System
校院名稱 成功大學
系所名稱(中) 醫學資訊研究所
系所名稱(英) Institute of Medical Informatics
學年度 101
學期 2
出版年 102
研究生(中文) 薛弘志
研究生(英文) Hung-Chih Hsueh
學號 Q56001074
學位類別 碩士
語文別 英文
論文頁數 78頁
口試委員 指導教授-郭耀煌
口試委員-李冠榮
口試委員-郭淑美
口試委員-盧文祥
口試委員-徐國偉
中文關鍵字 銀髮族  樂活  模糊集合  知識探索  異質性資料 
英文關鍵字 elderly  LOHAS  fuzzy set  heterogeneous data  knowledge discovery 
學科別分類
中文摘要 新世紀的危機議題,仍是圍繞在人口老化問題上。因此,活躍老化的健康概念是現階段專家們極為重視的議題,目的在於增加銀髮族學習以及從事休閒的機會。本論文在此議題下,發展一套虛實整合之植物栽培系統-伊甸園,透過系統以達成銀髮族休閒參與提升其心靈樂活指數。根據銀髮族使用上的需求,本篇論文亦提出了利用模糊集合理論與文字探勘技術來建立一個新穎的知識探索機制,從異質性資料中萃取隱含的知識,並探索最適合的栽培模式以輔助銀髮族進行植物栽培。不像傳統專注在處理文字的知識萃取方法,此研究額外考慮了文字特徵與數值特徵間關聯。在模型建立方面,利用上下文語意分析方法來定義文字特徵的類別,並透過統計區間估計方法來推估類別涵蓋的值域範圍,以完成模糊集合之知識萃取模型。另外,從知識萃取結果中探索適合使用者栽培模式,同時,我們更進一步透過使用者使用栽培模式後的結果(在此稱為使用者經驗)回饋,作進一步的探索機制調校,用以更符合使用者實際的使用需求。在本系統知識萃取的實驗中,利用專家問卷以評估萃取結果的合理性,並以專家判斷結果來評定萃取結果精準度,在結果顯示本論文方法可以達到85%精準度。在模式探索的實驗中,以模擬的方式模擬使用者實際使用模式後情形,且透過使用者經驗回饋進行探索機制調校,結果顯示調校後結果能符合使用者需求。最後,將透過銀髮族樂活指數量表來評估銀髮族使用系統後,其樂活指數的變化。
英文摘要 The crisis of the new century issues is still around in the aging population. Therefore, the concept of active is extremely important issue aging at this stage which encourages the elders to participate leisure activities and lifelong learning to enhance elder health vitality and life quality. In this issue, we develop a clicks-and-mortar system - Eden Garden which reaches to participate in leisure activities and enhance the mental LOHAS index. According to the elderly usage demand, we propose a novel knowledge discovery mechanism that employs the fuzzy set theory and text mining techniques. It aims to extract implicit knowledge from heterogeneous data and discovers the most suitable patterns to elders for plant cultivation. Unlike traditional approaches focused on textual knowledge extraction, our research is regarded the relations between nominal features and numerical features as analysis units. In this thesis, we use the context semantic analysis method to define the nominal feature category and estimate the cover range of each category via statistical interval estimation method. Finally, we are according to above steps to construct the fuzzy knowledge extraction model. In addition, we also discover the suitable patterns from the extraction results for user. Simultaneously, we further use the user experience feedback of tuning the discovery method to meet the users’ actual needs. In knowledge extraction experiment, we use the expert questionnaires to assess the reasonable of extraction results and use the expert judgment results as answers to calculate the extraction accuracy. The experiment results are shown that the accuracy of extraction method can be reached 85%. In pattern discovery experiment, we use the simulation way to simulate the actual usage status, then according to the user experience feedback to tune the original discovery method. The pattern discovery experiment results are shown that discovery method can meet users’ actual needs after tuning the mechanism via user experience feedback. Finally, we use the LOHAS index scale table to assess the mental index of elders, and observe the change of index after using the system.
論文目次 LIST OF TABLES X
LIST OF FIGURES XI
CHAPTER1. INTRODUCTION 1
1.1. MOTIVATION 3
1.2. ISSUES AND CHALLENGES 4
1.3. CONTRIBUTIONS OF THIS THESIS 5
1.4. ORGANIZATION 6
CHAPTER2. BACKGROUND AND RELATED WORK 7
2.1. HETEROGENEOUS DATA 7
2.2. KNOWLEDGE EXTRACTION 8
2.3. PATTERN DISCOVERY 9
2.4. HEALTHCARE OF ELDERS 10
CHAPTER3. SYSTEM ARCHITECTURE AND PROBLEM FORMULATION 12
3.1. SYSTEM SCENARIO OF EDEN GARDEN 12
3.2. SYSTEM ARCHITECTURE OF EDEN GARDEN 15
3.3. PROBLEM FORMULATION 16
CHAPTER4. A NOVEL KNOWLEDGE DISCOVERY MECHANISM 20
4.1. FUZZY KNOWLEDGE 21
4.1.1. Data Collection 22
4.1.2. Feature Selection 24
4.1.3. Missing Data 25
4.1.4. Category Definition 27
4.1.5. Interval Estimation 29
4.1.6. Fuzzy Set Design 30
4.2. INFERENCE ENGINE 36
4.2.1. Degree Calculation 36
4.2.2. Inference 37
4.3. DEFUZZIFICATION PROCESS 38
CHAPTER5. PATTERN DISCOVERY 42
5.1. PATTERN FREQUENT WEIGHT 42
5.2. USER EXPERIENCE FEEDBACK 43
5.3. RECOMMENDATION PATTERN 44
CHAPTER6. EXPERIMENTS AND DISCUSSION 49
6.1. EXPERT JUDGMENT 49
6.1.1. Experiment Design 49
6.1.2. Experiment Results Analysis 50
6.2. PATTERN DISCOVERY 52
6.2.1. Experiment Design 52
6.2.2. Experiment Results Analysis 53
6.3. LOHAS INDEX 56
6.3.1. Experiment Design 56
6.3.2. Experiment Results Analysis 57
CHAPTER7. CONCLUSIONS AND FUTURE WORK 62
REFERENCES 63
APPENDIX 69
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