臺灣博碩士論文加值系統

資料分析是由資料探測智慧的必經途徑，然而在商業的應用上卻充滿著各種挑戰，因而智慧型商業資料分析，就是針對一多變量的資料集，進行高層次概念分析模型的建立。此一結合統計與資料探勘運算技術的工具，克服了商業應用上常面臨的資料集變動頻繁、資料衡量尺度種類很多、資料欄位過多、資料欄位間關係複雜及模型配適不易等諸多問題，且所得出的模型具有統計上的可靠性，並能供領域專家很容易的進行解釋與評估。換言之，智慧型商業資料分析就是針對商業上的應用，結合了統計與資料探勘運算技術，且能滿足知識周全性原則的資料分析方法。
本研究以三種基本知識概念，包括關聯規則、結構方程式及列聯表等在商業上的應用，包括市場購物籃分析、知識的發現與創新及企業經營績效評量等問題，來進行智慧型商業資料分析方法與模型建構的探討。
本研究的主要目的即在發展智慧型商業資料分析方法及建構分析模型，這些模型克服了傳統統計與資料探勘方法在本質上的限制，滿足了知識周全性的原則，而將其整合在一個工具中，這些工具提供了使用者，可依問題的特性由資料庫中進行各種不同方式的知識發現工作，來建構可供解釋與決策所需的模型，以解決實務上所面臨的諸多問題，此即為本研究的主要貢獻。

Data analysis is on the unique route from data towards wisdom, while there exists various challenges for business applications, hence; intelligent business data analysis is just like a key to open a door which can get over those challenges during proceeding high level conceptual and analytical model construction against a multi-dimensional data set. Those data mining tools incorporate statistic and computational technologies can solve the problems that encountered in business applications such as large data volume, incomplete data, inconsistent data scale, huge number of data fields, complex relationships between data fields and model over fitting; and the resulting models that we obtained will be built with statistical reliability, and can be interpreted and evaluated easily by domain experts. In other words, intelligent business data analysis is the advanced knowledge discovery tools which incorporate statistic and computation, and fulfills the comprehensiveness rule of knowledge.
The research adopts 3 basic knowledge concepts including association rule, structural equation and contingency table, with their corresponding applications on business as market basket analysis, knowledge discovering and innovation and performance evaluation, to proceed the context exploration on intelligent business data analysis.
The main purpose of this study is to develop the analytical methods and model building for intelligent business data analysis. Those analytical models not only overcome the limitations of traditional statistic and data mining methods, but also fulfill the comprehensiveness rule of knowledge; and incorporate them into one tools set, those tools furnish the capability for user to proceed various knowledge discovery tasks by using database based on the distinction of problems. Models established by those tools can be interpreted and are useful for decision making to solve the problems that we encountered in the real world, and it is our contribution to the field of intelligent business data analysis.

第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
1.3 研究架構與流程 4
第二章 文獻探討 7
2.1 由資料探測智慧之過程 7
2.1.1 知識的特徵 8
2.1.2 知識的表現格式 9
2.2 商業資料探勘 11
2.2.1 知識發現的程序與目的 13
2.2.2 資料探勘在商業應用上的挑戰 14
2.3 多變量商業資料分析方法 16
2.3.1 關聯規則相關研究 16
2.3.2 創新知識探測 18
2.3.3 績效評量考核 21
第三章 關聯規則研究 26
3.1 局部關聯規則之建構 26
3.1.1 關聯資訊量計算 27
3.1.2 建立項目關聯聚類 32
3.2 屬性局部關聯分類法 36
3.3 關聯規則案例探討 42
3.3.1 市場購物籃案例分析 42
3.3.2 多屬性資料集局部關聯 44
3.3.3 多屬性資料集局部關聯規則分類 46
第四章 潛在結構模型探測研究 50
4.1 潛在結構模型之探測過程 50
4.1.1 尺度不變性(Scale Invariant) 52
4.1.2 潛在結構矩陣(Latent Structure Matrix，LSM) 52
4.1.3 結構殘差矩陣(Structure Residual Matrix，SRM) 54
4.2 潛在結構模型建構程序 54
4.3 潛在結構模型探測案例探討 56
4.3.1 完整變數集潛在結構矩陣 57
4.3.2 部份變數集潛在結構矩陣 60
第五章 績效評量研究 62
5.1 績效衡量與評估方法 62
5.1.1 尺度主成份分析 63
5.1.2 效率評估矩陣 65
5.1.3 效率評估排序法 67
5.1.4 效率對應分析 67
5.1.5 效率評估與排序程序 68
5.2 績效評量案例探討 70
5.2.1 效率對應與排序 71
5.2.2 效率評估矩陣線性規劃模型 75
5.3 績效排序探討 76
5.3.1 供應商評選 76
5.3.2 製造設備評選 82
5.3.3 藥局績效評估 84
第六章 結論與建議 90
6.1 結論 90
6.1.1 局部關聯規則 91
6.1.2 潛在結構模型探測 91
6.1.3 績效衡量評估 92
6.2 建議 94
6.2.1 局部關聯規則 94
6.2.2 潛在結構模型探測 95
6.2.3 績效衡量評估 95
參考文獻 97

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