臺灣博碩士論文加值系統

近年來由於製造技術進步和消費者需求的轉變，產品方案逐漸往多樣化發展。製造商為了因應不同需求，使每種產品在規格、功能或售價上有不同差別，以適應不同的製造技術以及消費者需求。由於多樣化的產品模式發展，造成產品相關的詳細資料量呈現增加的趨勢，包含產品設計、零件規格、價格、銷售量以及消費者評分等特徵資料。製造商透過蒐集這些資訊可以評估消費者的各類需求以及產品設計與製造之方向，並可依此調整產品設計以及其銷售策略。
因此本研究提出從網路蒐集產品開放資料，根據產品規格資訊等重要參數，使用集群分析法對產品進行分群分析，從不同的因素與特徵，了解產品間分群關係，可以作為未來產品設計的參考以及建議。完成集群分析後，再對分群結果加以檢定，以確定分群分析之方法為有意義的；且各群間有顯著差異性，未來產品設計時便可針對特定分群之特性，找出最重要之影響力因素，不僅在規格面向可以設計製造出具有特定功能及特性之產品，在顧客面向也可以規劃出較符合其期望之產品。設計與製造者可以根據分群分析結果了解不同消費者的偏好與需求，來改善及調整未來產品設計方案。在本研究中，研究方法模式經由實務驗證，比較各分群方法之差異，顯示研究方法模式為可行及有效，可以將分群模式應用於產品設計評估。

In recent years, due to technological advancements and shifts in consumer demands, product offerings have gradually diversified. Producers, in response to varied needs, differentiate each product in terms of specifications, features, or pricing to adapt to different manufacturing techniques and consumer demands. The diversification of product offerings has led to an increasing trend in the amount of detailed data, including product designs, component specifications, prices, sales volumes, and consumer ratings, being available as open data. Producers can use this information to assess various consumer needs and the direction of product design and manufacturing, adjusting product designs and marketing strategies accordingly.
Therefore, this study proposes collecting product data from the internet, conducting cluster analysis on products based on important parameters such as product specifications, to understand product clustering relationships from different factors and features. This analysis can serve as a reference and recommendation for future product design. After completing the cluster analysis and testing the clustering results, it is confirmed that the clustering analysis method is meaningful, and there are significant differences between groups. In future product design, specific characteristics of particular clusters can be
targeted to identify the most influential factors, allowing for the design and manufacture of products with specific functions and characteristics, meeting customer expectations. Designers and manufacturers can improve and adjust future product design schemes based on understanding the preferences and needs of different customers revealed through cluster analysis.
In this study, the research methodology model is validated through practical
experiments, comparing different clustering methods, demonstrating that the research methodology model is feasible and effective, and can be applied to product design evaluation.

目錄
摘要 ................................................................................................................... ii
ABSTRACT ..................................................................................................... iv
誌謝 ................................................................................................................... v
圖目錄 .............................................................................................................. xi
表目錄 ............................................................................................................ xiii
第一章 緒論 ..................................................................................................... 1
1.1研究背景與動機 ...................................................................................... 3
1.2研究目的.................................................................................................. 5
1.3研究流程.................................................................................................. 6
第二章 文獻探討 .............................................................................................. 7
2.1 智慧型手機 (Smartphone) ...................................................................... 7
2.1.1 手機的發展 ...................................................................................... 7
2.1.2 定義.................................................................................................. 8
2.2 產品屬性 (Product Attribute).................................................................. 9
2.2.1 定義.................................................................................................. 9
2.2.2 產品屬性之分類 .............................................................................. 9
2.2.2.1 依消費者需求層面分類 .......................................................... 10
2.2.2.2 依產品表現方式分類 .............................................................. 10
2.2.2.3 依內隱及外顯程度分類 .......................................................... 11
2.3 顧客關係管理 (CRM) .......................................................................... 11
2.3.1 定義................................................................................................ 12
2.3.2 顧客分類 ........................................................................................ 12
2.3.3 顧客關係管理架構 ........................................................................ 13

2.3.4 顧客關係管理於機器學習方面之應用 .......................................... 13
2.4 機器學習 (Machine Learning, ML) ...................................................... 14
2.4.1 K-Means .......................................................................................... 15
2.5 集群分析 (Cluster Analysis) ................................................................. 17
2.5.1 階層式集群分析 (Hierarchical Clustering) .................................... 20
2.5.2 非階層式集群分析 (Non-Hierarchical Clustering) ........................ 22
2.5.3 二階段方法分群分析 ..................................................................... 22
2.6 文獻小結 ............................................................................................... 23
第三章 研究方法 ............................................................................................ 24
3.1 研究架構 ............................................................................................... 24
3.1.1 研究範圍與限制 ............................................................................ 25
3.2 資料來源以及前處理 ........................................................................... 25
3.2.1 蒐集產品數據 ................................................................................ 25
3.2.2 數據前處理 .................................................................................... 26
3.3研究方法................................................................................................ 26
3.3.1 集群分析 (Cluster Analysis) .......................................................... 30
3.3.2 各演算法分群結果評量準則 ......................................................... 32
3.3.3 K-Means .......................................................................................... 33
3.3.4 區別分析 (Discriminant Analysis) ................................................. 33
3.3.5 混淆矩陣 ........................................................................................ 36
3.3.6 單因子變異數分析 (One-Way ANOVA) ....................................... 36
第四章 資料分析與結果 ................................................................................ 37
4.1 實驗設計 ............................................................................................... 37
4.1.1 資料蒐集 ........................................................................................ 39
4.1.2 資料前處理 .................................................................................... 39

4.1.2.1 資料文字轉換 ......................................................................... 40
4.2 資料標準化 ............................................................................................... 41
4.3 產品分群 .................................................................................................. 41
4.3.1 各集群分析法最適之分群數 ......................................................... 64
4.3.2 機器學習演算法最適之分群數 ..................................................... 67
4.4 集群分析 .................................................................................................. 67
4.4.1 集群分析演算法決定分群數目 ..................................................... 69
4.4.2 K-Means 集群分析 ......................................................................... 69
4.4.3 區別分析 (Discriminant Analysis) ................................................. 69
4.4.3.1 混淆矩陣 ................................................................................. 70
4.4.3.2 決定成員應於何群之設定 ...................................................... 72
4.5 單因子變異數分析 ................................................................................... 72
4.5.1 單因子變異數分析 ........................................................................ 75
4.6 分群結果整理 ........................................................................................... 75
4.6.1 價值取向型 .................................................................................... 76
4.6.2 經濟實惠型 .................................................................................... 77
4.6.3 低價型 ............................................................................................ 77
4.6.4 精緻型 ............................................................................................ 77
4.6.5 限量型 ............................................................................................ 78
4.7 本章小結 .................................................................................................. 78
第五章 結論與未來建議 ........................................................................................ 80
5.1 研究結論 .................................................................................................. 80
5.2 行銷建議 .................................................................................................. 81
5.3 研究限制 .................................................................................................. 81
5.3.1 取得研究資料之限制 ..................................................................... 81

5.3.2 分群變數之限制 ............................................................................ 82
5.4 未來研究建議 ........................................................................................... 82
5.4.1 資料探勘技術及研究主題方面 ..................................................... 82
5.4.2 資料範圍方面 ................................................................................ 82
5.4.3 購買策略方面 ................................................................................ 82
參考文獻 ................................................................................................................. 87

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