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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/87579完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 潘建興 | zh_TW |
| dc.contributor.advisor | Frederick Kin Hing Phoa | en |
| dc.contributor.author | 李奕宏 | zh_TW |
| dc.contributor.author | Yi-Hung Li | en |
| dc.date.accessioned | 2023-06-20T16:10:20Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-06-20 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-01-17 | - |
| dc.identifier.citation | [1] Huang, C., et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The lancet, 2020. 395(10223): p. 497-506.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/87579 | - |
| dc.description.abstract | 隨著2019年新型冠狀病毒的傳播帶來全球性的災害，科學家們開始意識到適合流行疾病分析的機器學習方法論之重要性。而在眾多相關演算法的發展之中，此研究著重在一個適合時間序列資料分析的分群法—「基於模型的遞迴分割法」之時間序列版。在這篇研究中，我快速地探討了幾篇和這個演算法的發展還有一些與本篇發想的方法論有關的文獻，嘗試優化前篇研究所提出之R/Shiny視覺化工具，並且提出了兩大優化這個演算法的方法論，其中包含了基於人口的標準化技巧，以及鄰近域相關特徵的衍生性概念。以台灣一年半的新型冠狀病毒每日確診數資料為研究對象，我不僅在應用此演算法及上述方法論的實驗中得到了一些流行相關的洞見，也透過了統計方法驗證了我的方法論的有效性。完整地分析過實驗後，我針對這個演算法的分群任務和預測任務都個別提出了相對應的建議參數設定。最後，針對開發過程和實驗過程中的限制，我列舉了幾項本研究未來可能可以優化的方向。 | zh_TW |
| dc.description.abstract | The outbreak of COVID-19 raised awareness of the need to develop machine learning methodology suitable for epidemiological pattern recognition. This study focuses on time-series model-based recursive partitioning and one of its derivative applications for visualization. Several literature reviews on related studies including the evolution of this methodology are provided. This study documents the improved functionality enhanced user-friendliness of an R/Shiny application for visualizing time-series model-based recursive partitioning and proposes several methodologies to strengthen the computational results from this algorithm. With Taiwan township-level COVID-19 spread data, several experiments were conducted to gain insights on the spread patterns and validate the effectiveness of this study’s main contributions, which include the population-based scaling technique and the derivative concept of adjacent domain-relevant attributes. Finally, various option configurations for clustering and forecasting tasks based on this epidemic-related algorithm are suggested. After considering the limitation of this work, several promising future directions are provided at the end of this study. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-06-20T16:10:20Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-06-20T16:10:20Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 論文口試委員審定書 i
Acknowledgements ii 摘要 iii Abstract iv Contents vi List of Figures viii List of Tables ix Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Background 3 1.3 Objectives and Organization 6 Chapter 2 Literature Review 9 2.1 Model-based Recursive Partitioning 9 2.2 Time Series Clustering using Domain-Relevant Attributes 12 2.3 An Interactive Approach to Time Series Clustering 13 2.4 Data Removal and Missing Value Imputation 14 Chapter 3 Interactive Programming by R/Shiny Apps 17 3.1 Introduction to R/Shiny Apps 17 3.2 The Use of R/Shiny Apps in Previous Work on COVID-19 Research 18 3.3 The Improved Version - Interpretable Pattern Recognition Software 19 Chapter 4 Experimental Studies 22 4.1 Origin of Data Feature 22 4.2 Data Preprocessing 24 4.3 Experimental Designs 25 Chapter 5 Results and Interpretations 30 5.1 Effectiveness of Adjacent Domain-Relevant Attributes 31 5.2 Effectiveness of Population-Based Scaling Methods 35 5.3 Differences between Different Pruning Criteria 40 5.4 Suggested Option Configuration 41 5.5 Comparison with the Previous Study 43 Chapter 6 Discussion and Conclusion 45 References 49 Appendix A — Experiment Results 54 | - |
| dc.language.iso | en | - |
| dc.subject | 基於模型的遞迴分割法 | zh_TW |
| dc.subject | 時間序列 | zh_TW |
| dc.subject | 視覺化工具 | zh_TW |
| dc.subject | 新型冠狀病毒 | zh_TW |
| dc.subject | model-based recursive partitioning | en |
| dc.subject | COVID-19 | en |
| dc.subject | time series | en |
| dc.subject | Shiny | en |
| dc.title | 以時間序列分群法結合R/Shiny資料視覺化探討COVID-19傳播 | zh_TW |
| dc.title | An Investigation to the Spread of COVID-19 via Time Series Clustering and its Data Visualization via R/Shiny App | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.coadvisor | 吳沛遠 | zh_TW |
| dc.contributor.coadvisor | Pei-Yuan Wu | en |
| dc.contributor.oralexamcommittee | 陳君厚;顏佐榕;郎慧珠 | zh_TW |
| dc.contributor.oralexamcommittee | Chun-Houh Chen;Tso-Jung Yen;Hui-Chu Lang | en |
| dc.subject.keyword | 基於模型的遞迴分割法,新型冠狀病毒,視覺化工具,時間序列, | zh_TW |
| dc.subject.keyword | model-based recursive partitioning,time series,Shiny,COVID-19, | en |
| dc.relation.page | 56 | - |
| dc.identifier.doi | 10.6342/NTU202300086 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2023-01-18 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資料科學學位學程 | - |
| dc.date.embargo-lift | 2025-01-10 | - |
| 顯示於系所單位： | 資料科學學位學程 | |
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