分類問題是資料採礦作業中最普遍的一種，其目的在於事先預測「尚未發生」的分類事實，信用卡違約風險預測模型正是分類問題的一項應用。本研究將以商業智慧的觀點，配合Microsoft SQL Sever 2005軟體所提供的資料採礦工具，利用發卡銀行龐大的客戶歷史資料，透過先進的資料採礦技術(決策樹、類神經網路、貝氏機率分類)和統計方法(羅吉斯迴歸)，建構出一套完全符合自身銀行客戶特性的信用卡評分模型之流程。 以本研究的結果所示，在模型的預測能力上，羅吉斯迴歸優於類神經網路，類神經網路又優於貝氏機率分類及決策樹，且根據BASELⅡ對信用評分卡的規定，羅吉斯迴歸為其標準的演算法，因此最終模型即選擇以羅吉斯迴歸所建立的模型。

本研究定義運用技術指標建構投資決策之步驟，明確描述各步驟細節，投資人根據此流程定義，可利用技術指標逐步運算出投資標的之投資價值，作為最終投資決策之依據。同時，本研究建立技術指標、偵測機制等分類架構，讓投資人主觀的投資需求對應（map）到技術指標，建立個人化的投資決策。 / This paper defines the stages that how to build an investment decision with technical indicators and describes the details of each stage definitely. According to the process definition, investors can calculate the investment value of the investment target with technical indicators step by step. The investment value can be the foundation of the final investment decision. This paper also establishs both classificaton models of technical indicators and detect mechanisms. It makes investors map their subjective demand for investment information to technical indicators, personlize their investment descions.

近幾年來資料採礦及雲端運算的興起，導致許多公司企業紛紛推出有關雲端運算的服務，或利用資料採礦的技術以助於了解客戶行為。而資料採礦的技術不僅是企業所獨享的一個工具，一般非企業的使用者也常常會面臨到決策問題，為了讓一般使用者能夠方便取得軟體工具以及節省時間成本，本研究以雲端運算為概念，利用RExcel軟體和Excel VBA程式語言為研究工具，發展出一個資料採礦分類雲端運算系統。 　　本研究將欲分類的目標變數分為三種型態：數字連續型、數字類別型以及文字類別型，此分類系統會依照目標變數型態的不同，而採取不同的分類模型來分析使用者之資料，並分別以三個資料檔為例，上傳至此資料採礦之分類系統進行分析後，其分析結果報表將以網頁預覽的方式呈現給使用者，使用者可以針對連續型目標變數的資料分析結果，利用MAPE值評估分類模型之優劣，而類別型目標變數的資料分析結果，則可以正確率來評估分類模型之優劣。 　　使用者可透過簡易步驟來操作此系統，並選擇可解釋資料之最佳模型，也可從結果報表中獲取資料之特性，更進一步地可以進行所需的決策。 關鍵字：雲端運算、資料採礦、分類模型 / In resent years, the rise of data mining and cloud computing has led many enterprises have been offering services related to cloud computing, or using data mining techniques to understand customer behaviors. Data mining is a tool not only for enterprises, but also for general non-business users who often face making decisions. In order to enable general users to easily assess the software and save time and costs, this study proposes a classification system of data mining constructed by RExcel and Excel VBA, which is based on cloud computing. 　　In this study, the target variable is divided into three types: digital continuous, digital categorical and literal categorical. The classification system is in accordance with the different types of target variables, taking different classification models to analyze user’s data. Taking three data as examples, respectively, uploading them to the system, then the analysis results will be present to the user in the way of page preview. The user can use MAPE values to evaluate classification models with regard to the results of the data for the continuous target variable, and use correct rate to evaluate classification models with regard to the results of the data for the categorical target variable. 　　Users can take simple steps to operate the system, select the best model which can explain the data, and obtain the characteristics of the data from the result reports, further to the necessary decision-making. Keyword: cloud computing, data mining, classification models

電腦實驗(computer experiment)是探索複雜系統輸出反應值和輸入參數之間關係的重要工具，其重要特性是每一次的實驗非常耗費時間及運算的成本。一般在電腦實驗中，研究者較常關心的多是反應曲面的配適和輸出反應值的最佳化等問題(如極大或極小值)。借由一真實平行分散處理系統的啟發，本文所關心的是如何找出系統反應值的局部目標區域。此目標區域有一個非常重要的特性，即區域內外的輸出值所呈現的反應曲面並不連續，因此一般傳統的反應曲面法(response surface methodology)無法適用。本文提出一個新的、可估計不同類型電腦實驗目標區域的有效方法，其中包含了逐步均勻設計和建立分類模型的概 念，電腦模擬的結果也證明了所提方法準確又有效率。 / Computer experiment has been an important tool for exploring the relationships between the input factors and the output responses. It’s important feature is that conducting an experiment is usually time consuming and computationally expensive. In general, researchers are more interested in finding an adequate model for the response surface and the related output optimization problems over the entire input space. Motivated by a real-life parallel and distributed system, here we focus on finding a localized “target region” for the computer experiment. The experiment here has an important characteristic - the response surface is not continuous over the target region of interest. Thus, the traditional response surface methodology (RSM) cannot be directly applied. In this thesis, a novel and efficient methodology for estimating this type of target regions of computer experiment is proposed. The method incorporates the concept of sequential uniform design (UD) and the development of classification techniques based on support vector machines (SVM). Computer simulation shows that the proposed method can efficiently and precisely estimate the target region of computer experiment with different shapes.