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研究生:陳姿潔
研究生(外文):Tzu-Chieh Chen
論文名稱:透過資料探勘技術探究建築風格意象之差異-以台中市七期重劃區為例
論文名稱(外文):Evaluation Difference of Data Mining on the Style of Buildings in the 7th Land Consideration District in Taichung City
指導教授:聶志高聶志高引用關係陳重臣陳重臣引用關係
指導教授(外文):Chih-Kao NiehJong-Chen Chen
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:資訊管理系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:88
中文關鍵詞:屬性選取自組織特徵映射關聯式法則建築外觀評價類神經網路(多層感知器)決策樹
外文關鍵詞:self-organizing mapassociation ruledata miningdecision treeevaluation of building façadeattribute selection
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美學和建築在我們的日常生活中是兩個很重要的因素,但在某些情況下,都制定了一個獨立的方式進行。近年來,建築和美學逐漸在彌合中,並且也愈來愈受到重視。由於建築外觀對評價者會產生不同的影響,因此,本研究將設計學院先前透過田野調查的問卷資料進行分析,總人數為203人,其中分為專業人士與非專業人士兩大部份。本研究使用資料探勘方法包括決策樹、多層感知器、屬性選取、關聯式法則和自組織特徵映射。本研究決策樹的目的是基於專業人士與非專業人士在背景差異上的不同,例如年齡、性別、學習年資、職業等,造成評價上的差異先進行分類,以找出影響專業與非專業人士評價這十四棟建築的主要決定因素。再透過屬性選取,便可了解在二十組相對形容詞中,主要影響專業與非專業人士評價十四棟建築的關鍵形容詞有哪些。本研究結果顯示,大體而言,專業與非專業人士在評價這十四棟建築時的想法確實存在些微差異,只是差異並不大,而在性別想法上,亦無明顯差異。因此,透過關聯式法則的分析,我們更可以從中了解到對於專業以及非專業人士而言,哪些形容詞屬性是屬於高度相關的。
This paper presents a method based on decision tree classifier to identify the evaluation of professional and unprofessional people. Considering that the feature of buildings facades maybe have a effect on the evaluation of the people. Aesthetics and architecture are two important elements in our daily life, but in some cases are developed in an independent manner. Architectural and aesthetics are gaining more attention in recent years. However, the past researches are rare on the relationship of the features perceived by people in different groups, including professional training, ages, and so on. The purpose of this study is to perform data analysis of an aesthetic data set, in which the data were collected from a field study conducted through a questionnaire on fourteen categories of buildings.
There were 203 volunteers, professional and unprofessional, participating this study. Different data-mining tools were employed to this data set, including WEKA 3.4 ---J48 decision tree, multilayer perceptron, association rule, attribute selection and Neural Network Based Clustering using Self Organizing Map (SOM) in Excel. The purpose of this study is to find the differences between professional participants and unprofessional participants. Decision tree is designed to classify the evaluation differences between professional and unprofessional people. Using attribute selection, we could realize the key adjectives for professional participants and unprofessional participants. From experiments, our findings showed that professional and unprofessional people possessed slightly difference on 14 buildings evaluation of idea, in general. Based on the findings, some interesting relationships in large sets of data by using association rules to mine the interesting rules.
摘要.............................................i
Abstract.........................................ii
誌謝.............................................iv
Table of Contents................................v
List of Illustrations............................vii
List of Tables...................................ix
1. Introduction.................................1
1.1 Background and Motivation...................1
1.2 Research Goal...............................2
1.3 Outline of the Thesis.......................2
2. Literature Review............................3
2.1 Image, aesthetics in architecture...........3
2.2 Data mining.................................4
2.3 Decision Tree---J48.........................5
2.4 Self-organizing Map.........................8
2.5 Association Analysis.......................10
3. Research Method.............................11
3.1 Pre-processing.............................12
3.2 Clustering.................................13
3.2.1 Self-Organizing Map (SOM)................14
3.2.2 Association Rules........................15
3.3 Classification 15
3.3.1 Decision Tree............................16
3.3.2 Multilayer Perceptron (MLP)..............18
3.4 Participants and Instruments.......19
3.4.1 Participants.............................19
3.4.2 Instruments..............................20
4. Experiment Results..........................23
4.1 Decision Tree..............................23
4.2 Multilayer Perceptron (MLP)................29
4.3 Attribute Selection........................33
4.4 Self-Organizing Map (SOM)..................35
4.5 Association Rule...........................40
4.6 Conclusion.................................48
5. Conclusion and Implications.................51
5.1 Conclusion.................................51
5.2 Recommendations for future research........53
References......................................54
Appendix A......................................56
Appendix B......................................60
Appendix C......................................62
Appendix D......................................72
Appendix E......................................75
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