數位化科技帶給人們生活莫大的便利，科技的日新月異，現代人已經很少再像過去一樣靠著寫日記描繪出生活的點點滴滴，而是利用「拍照」來留下想要回憶的時刻，而數位相機正是現代人記錄生活的道具。根據產業情報研究所預估，2009年全球數位相機出貨量約為1.2億台，而過了2010年後，數位相機將會有穩定上升的成長率，本研究找出購買數位相機的消費者個人特色，深信藉此對於行銷上會有很大幫助。 本研究使用使用4種模型建置方式，分別為C5.0、CART、類神經網路和K-means模型，從模型結果中找出數位相機在市場上消費者的共通特性，並依照這些特性擬訂不同的行銷手法。 經由分類矩陣比較3種模型之優劣，最後建模結果顯示C5.0模型為三者模型中的最佳，所以本研究選定C5.0為最後的解釋模型。C5.0選出前10項影響「是否購買數位相機」的重要變數，分別為「搜尋資訊」、「樂活自足」、「網路購物」、「初戀」、「出國旅行」、「年齡」、「家庭月總收入」、「收聽網路電台或音樂」、「個人月可支配所得」、「實用特性」，本研究依據數位相機的產品、價格、通路、推廣等項目提供行銷組合分析。另外，將K-means模型分成三群後，樂活務實群和資訊達人群中，受訪者購買數位相機比例分別為48.17%和49.11%，因此參考此兩群的受訪者特性和C5.0的結果，找出會購買數位相機者的特質，針對這些特質制訂一些行銷策略，希望可以提供給數位相機廠商或其他研究者參考。

台灣地狹人稠造成台灣機車的使用率相對於其他國家要來的高，因此許多機車製造商如雨後春筍般的出現成長來應付一般消費大眾的需求。但是藉由資料可以發現，台灣機車的銷售量是以負成長的速度前進，除此之外台灣並無世界知名品牌來支持未來銷售。此研究目的於使用SPSS Clementine 12.0找出購買機車行為重要之變數。變數分為兩群；一群為基本人口資料變數，另一群為消費者購買行為變數。藉由此兩群變數可建立統計模型: CHAID,，類神經的準確率相較於Logistic regression，C5.0和C&RT要來的高，因此此兩種模型可成為之後解釋之基準。 透過CHAID，“資產規模”、 “婚姻狀況”、 “物美價廉”、 “節能減碳”、和 “時尚流行”為五種重要變數。接下來透過三陽機車和哈雷機車case study將可提出行銷策略上建議。

本研究根據過去藥局經營文獻中，所提出之藥局經營關鍵因素進行數據實證，以了解在過去訪談或其他質化研究中所整理之藥局經營關鍵因素是否能實際影響藥局的經營狀況。 　　由研究結果，利用Y藥局之POS資料實證：具有不同特性的消費族群對Y藥局而言，具有不同的顧客價值以及消費行為，實證過去文獻所提出之結論。 　　而針對不同客群的分群方式，本研究利用購買品類的頻率及數量，將顧客做集群分析，共分出五種類型：家庭育兒族、新婚未孕族、高齡保健族、新生兒養育族及愛美小資族。整體而言愛美小資族因為購買產品之特性，有較高的顧客終生價值，且在購買行為上，以購買多樣性的表現顯著高於其他族群，但在購買頻率及近期性表現則較差，顯示出該族群有較多的可能至其他通路購買。而連鎖藥局的主要顧客，家庭育兒族，則有最低的顧客價值，在購買行為上品類較為單一，數量零碎雖購買行為頻繁，卻未能實際創造價值。 　　另外針對藥局經營關鍵因素也發現，藥局在活動促銷上，主要能促進消費者的購買頻率及縮短消費者購買的近期性，整體而言是能提升短期的消費次數，但在金額上卻未有較顯著的相關性，也顯示了金額的促銷並未能持續將顧客價值提升，僅能刺激短暫的消費行為。 　　針對未來相關研究之建議也認為，透過資料採礦，能有效將實際銷售資料轉化為消費行為的刑為變數，為未來藥局經營做更多實際數據的驗證，改善過去多使用店長經驗及質性訪談方式衡量經營成效之狀況　。 / As the concept of the Customer Relationship Management (CRM) is getting more and more popular. The analysis way of data mining used not only extrapolating data but revealing the meaning of what the customer will think and what kind of customer will be the most valuable. This research focuses on the study of utilizing the model of RFM which meaning regency, frequency and monetary. The research not only using three measures but also adding the profit of each customer to find who the most valuable one is. And establish a better way to cluster the customer then finding the best marketing strategy for them. According to understand the pareto principle of the 80/20 rule, the research combine RFM model and cluster to measure the customer value. As well as analyzing the basic information of customer and the transaction data to understand the customer’s buying behavior and provide customer suitable products and services. The result showed that, the different types of pharmacy customers have different customer value which confirming the past researches. According to the research purposes we also clustering the pharmacy customer in five types which are family, newly-married, petty budget, elder and pregnancy. And the result showed that the petty budget is the most valuable cluster in all of the pharmacy customers.

由於台灣國人所得提高，生活水準跟著日漸提高，近年來更是意識到健康對個人及家庭的重要性，因此國內健康食品與藥品市場在這幾年蓬勃地發展，特別是連鎖藥妝的普及，結合藥品、健康食品與開架式保養品、化妝品銷售，提供專業藥師諮詢服務，成為複合式的經營模式。但近年來連鎖藥妝零售業者也面臨來自外商連鎖藥妝、本土連鎖藥妝、地區性連鎖藥局等不同體系的競爭，因此藥品及化粧品零售業者普遍認同，目前經營上所面臨之困難主要為「同業競爭激烈」。 商品力為一連鎖藥妝零售業者成功的重要因素，具體展現在商品多樣性、商品獲利性、商品價格競爭力、商品獨特性…等不同的面向。目前藥品及化粧品零售業中，確實大部分的業者都有商品企劃或設計的需求，但有商品企劃或設計部門者僅為少數。利用資料採礦技術，將能在不大量增加人事費用的情況下，有效率地協助進行商品企劃或設計，進而提升連鎖藥妝零售業者的商品力。 本研究將針對資料採礦在連鎖藥妝上的應用進行探討，包含以下研究目的： 1. 利用資料採礦中之集群分析建置商品力矩陣，代表他們的屬性與價值。透過商品力矩陣釐清各商品的定位，幫助決策者優化商品組合，針對各商品執行妥善策略安排。 2. 依循集群分析後的結果，更進一步進行商品分類的關聯規則分析。幫助決策者將集群分析之成果化為實務決策之參考，優化商品組合，針對各商品執行妥善策略安排，也為關聯規則的整理帶來新的應用方式。 3. 根據上述兩模型建置之結果，對H連鎖藥妝提出具體可行之行銷策略建議。 本研究利用資料採礦中的Two-step Cluster模型建置出H連鎖藥妝中各項商品的商品力矩陣，此矩陣的兩軸分別為「個別商品的平均毛利」及「個別商品的年交易筆數」，將各種商品概略分為明星、樂透、忠狗、問號四大類商品，分別代表他們不同的屬性與價值。同時配合關聯規則分析，提出具體可行之候選規則篩選模式： 1. 樂透型商品，應用方式有兩種，將樂透型商品放在Apriori模型中的後項，找出導購向樂透型商品的潛在模式；將樂透型商品放在Apriori模型中的前項，並將後項商品作為加價購搭售促銷標的，提升購買樂透型商品的意願。 2. 忠狗型商品，應用方式也有兩種，將忠狗型商品放在Apriori模型中的前項，找出可能導購的商品標的，推出合適的加價購搭售促銷活動；另外也可以藉由觀察忠狗型商品的消費行為，進而提供適當的促銷、推薦，提高其他品項交叉銷售的可能性。 / Taiwanese living standard raised due to the income growing, which lead to recognizing the importance of health toward personal and family. As a result, the market of dietary supplements and drugs flourishing these years, especially the spread of chained drugstores, which turned into combinative store by providing professional pharmacist consultant and selling of drugs, dietary supplements, skincare products and cosmetics. The drug and cosmetic retailers generally agreed that the main difficulty is “Industry Competition” due to the competition from different systems, including foreign chained drugstores, local chained drugstores and regional chained drugstores. Commodity competitiveness is one of the key successful factors of chained drugstores, which expressed as commodity diversity, commodity profitability, commodity price competitiveness, commodity uniqueness, etc. Seldom drugstores own product planning or designing department although most drugstores have demand of product planning or designing. It could raise the commodity competitiveness of chained drugstores by applying data mining to help product planning or designing more efficiently without increasing too much labor cost. This study focus on the application of data mining on chained drugstores, including goals below: 1. Building commodity competitiveness matrix by cluster analysis, representing their features and values. Through positioning products on commodity competitiveness matrix, helping decision maker optimize product mix and execute appropriate strategy toward products. 2. Based on the results from cluster analysis, proceed association rules analysis toward product categories. Help turning the results from cluster analysis into references of actual decision, optimize product mix and execute appropriate strategy toward products. Bringing new application pattern of association rules analysis. 3. Providing actual marketing strategy suggestions to H chained drugstore based on the two models built above. This study built commodity competitiveness matrix of H chained drugstore by Two-step Cluster model, which take “average margin of individual product” and “annual transaction amounts of individual product” as two axes. Divided products into Star, Lottery, Greyfriars and Question Mark. Each of them represent different features and values. Providing practical filtering rules of candidate rules in association rules analysis: 1. Lottery Products: Placing lottery products as consequents in Apriori model, searching for the potential pattern led to buying lottery products. Placing lottery products as antecedents, which we can provide the consequents with additional purchase discount in order to raise the willing to buy lottery products. 2. Greyfriars Products: Placing Greyfriars products as antecedents, searching for potential recommendation with additional purchase discount. Providing appropriate sales and recommendation to raise the possibility of cross-selling by observing consuming behaviors of Greyfriars products.

關係行銷或是一對一行銷是目前行銷領域上廣泛被討論的議 題，企業要如何透過有效的辨識、區隔、互動以及客制化來量身打造 顧客專屬的個人化產品與服務內容，並強化其重複消費動機及忠誠， 為目前各種產業爭相積極追求的目標，此外，由於微利時代風暴，各 產業無不希望透過顧客價值的辨識與經營，實現以更有效、更低的成 本的差異化行銷策略來創造高收益的企業經營目標，以航空產業如此 資本密集，高固定成本，低變動成本以及不對稱的供需平衡，誰掌握 低成本領導與差異化策略優勢，便能決戰存續於二十一世紀超競爭時 代之中。 由於資訊科技、網際網路以及資料探勘技術的臻於成熟， 充份 發揮了跨國、即時、深度滲透與互動的特性，使得關係行銷、一對一 行銷的實現變得更加有效而可行。本研究希望從顧客價值的認定、顧 客忠誠策略以及資料探勘技術的探討，來思考如何運用於航空公司會 員系統的顧客區隔，同時，希能透過航空公司產業通路架構、全球旅 行社訂位系統(CRS)的發展現狀、微妙的航空公司間策略聯盟以及不 同航空公司所提供的會員酬賓計劃內容的探討與陳述，初略地對個案 公司的所在環境進行策略性分析，以建議其所需採取投入關係行銷的 主要焦點客層。 緊接著， 利用資料探勘工具中的分群技術， 選定有效的指標變 數，針對某一區間的會員交易資料進行分群，藉由研究各群會員所蘊 含的特殊屬性，如營收貢獻、產品特性、通路喜好以及消費行為等等， 依據前述所定義的目標客層，以創造顧客價值為目標，精確建立目標 客戶群，並據以設計不同的行銷策略與產品組合，逐步深耕建立完整 會員關係行銷資料庫。 最後， 對於本研究所無法觸及的研究議題， 概略指出後續可能 的研究方向與建議。 / Customer Relationship Management and data mining in this hyper-competitive era have revealed a lot of interesting and innovative opportunities to enrich the capability of company to realize and provide customer value. They touch the most critical issue of the enterprise, “How can we create and sustain successful advantage, and maximize profitability by leveraging new technologies ?＂In this thesis, we will focus on the application of data mining in the FFP of the airlines industry, and look over the differences among FFP members to discover the implicative needs of FFP customers. First of all, we start discussion on literature review in chapter two, which was divided into three parts: customer loyalty strategy, customer value and data mining. In this chapter, we put emphasis on the concepts and definitions of above topics, and they would be helpful to us to select and decide key variables in the following data mining practice. Chapter three of this thesis is to introduce the structure and characteristics of the airlines industry, the history of Computerized Reservation System(CRS), the airlines strategy alliance and the FFP system, and to figure out the way to understand the existing threats and opportunities. Chapter four, which was abode by the steps of data mining process, defines business issues and collects around one year's FFP historical transaction data to establish the target data and perform an actual data mining practice. In this real practice, we use the demographic cluster function of IBM Intelligent Mining tool to do member clustering. We select net revenue, first and business class spending rate, reservation booking designator and customer activation as analytical variables to perform FFP member clustering. Each variable has been well equipped with weight and method to produce best cluster pattern. Finally, according to the mining results we have explored and interpreted, we provide our draft recommendations about marketing planning and mix activities from the perspectives of FFP members clustering.

當越來越多的藥物被發展出來進而治療人類的疾病，人們使用藥物時只知道該藥物對疾病的療效，卻忽略了其中可能隱藏的危機，不只是西藥，連中藥也隱藏相當大的危機。雖然國內有藥品管制局進行藥物交互作用的控管，但是藥物交互作用真的被有效控管嗎？成效又如何呢？有鑑於此，這成為大家都想知道的問題。 利用健保資料庫中龐大的門診資料，嘗試推估國人藥物交互作用的情形，由國人十大死因中得知，其中尤以慢性病病患的情形更為嚴重，例如：惡性腫瘤、腦血管疾病、心臟疾病、糖尿病、肝病和高血壓，本次研究以心血管病患為討論主體，試瞭解在長期服用心血管藥物時，醫生在已知病患病情之下卻無法避免藥物交互作用的情形為何，利用資料採礦的方法，找出可能造成交互作用發生的原因、族群，進而去提醒醫療機構避免其發生。 研究發現，心血管疾病病患分為一些併發症族群，如：心臟病、心血管疾病糖尿病、關節炎、消化潰瘍，從這些病患用藥中發現許多交互作用的用藥與其併發症也許多相關。高血壓疾病病患大多以強心劑、利尿劑為主的交互作用，心血管疾病併發糖尿病病患大多以口服降血糖為主的交互作用，心血管疾病併發關節炎病患大多以解熱鎮痛劑為主的交互作用，而心血管疾病併發消化潰瘍病患大多以制酸劑為主的交互作用。 健保局應當面對且對交互作用之高危險族群作一共同用藥規則，將用藥危機降到最低，讓心血管病患在治療的過程中，得以享有良好的醫療品質，亦不造成藥物濫用、浪費之情形。 / As more medicines are being developed to cope with diseases, most of the users think of the therapeutic effect of the medicine without thinking of the risk that might have associated with drug interactions. The risk of drug interactions could have set chemical reactions thereby causing drug side effects. Although the National Bureau of Controlled Drugs has set a procedure for controlling drug interactions, the issue of validity and efficiency remains an open question. The trend of people contracting chronic diseases is on the rise, one of which is cardiovascular. The attempt of this paper is to observe for the effect of drug interactions if any for the long term usage of the cardiovascular medications under the supervision of the doctors. We adopt data mining techniques to single out the probable variable causes in triggering negative effect of drug interactions and presented them to related medical personnel so that a tightening measure is adopted when administered the medications. The data used in this research comes from the National Health Insurance Research Database. Our research findings have reviewed that cardiovascular disease patients suffering from the complications, such as diabetes, arthritis, peptic ulcer and hear disease are highly related to the drug interactions. Heart disease patients are at risk of cardiac stimultants and diuretics. Diabetic’s patients taking the Sulfonylureas suffer from its interactions. And arthritis patients are at risk of having the side effects of taking Aspirin. Bureau of National Health Insurance should set a standard procedure in monitoring the prescription given by the medical personnel in an effort to reduce the risk of drug interactions and set off the stage of quality medical treatment to keep off the abuse of drug usages.

In the wake of growing database that has already become the trend of today’s business environment within the foreseeable future, reviewing quality information from mountains of data residing on corporations or organizations’ network such as sales figures, manufacturing statistics, financial data and experimental data is clearly costly, time consuming and definitely ineffective approach. Therefore we would need a sound and effective method in obtaining only portions of the data that are representative to the population and which allow us to build the reliable model based upon the sampled data. However, sometimes we have a situation where the database is of limited in size, under such circumstance, we initiate the idea which is relatively new to adding the attributes or values into the database to enhance the quality of the data Follow through such a procedure; it is obvious that implementing a good sampling method is an important groundwork leading us to reach final destination that is obtaining a reliable predictive model. And this is our research goal that is to get an effective and representative value-added sample of by means of sampling method for building an accuracy predictive model. The concept is pretty straightforward that is if we want to get good predictive samples then we need the correct sampling methods. The sampling methods under study are simple random sample, system sample, stratified sample and uniform design. The models used are the C5.0, logistic regression, and neural network for categorical predictive variable and stepwise regression for continuous predictive variable. The results are discussed in the conclusion section. Keywords: Database、Data Mining、Sampling、Value-added database

資料在企業資訊來源中扮演了極為重要的角色，特別是在現今知識與技術的世代裡。如果對於一個有意義且具有代表性資料庫中的遺漏值能夠正確的處理，那麼對於企業資訊而言，是一個大有可為的突破。 然而，有時我們或許會遇到一些不是那麼完善的資料庫，當資料庫中的資料有遺漏值時，從這樣資料庫中所獲得的結果，或許會是一些有偏差或容易令人誤解的結果。因此，本研究的目的在於插補遺漏值為資料庫加值，進而根據遺漏值類型建立插補模型。 如果遺漏值為連續型，用迴歸模型和倒傳遞類神經模型來進行插補；如果遺漏值為類別型，採用邏輯斯迴歸、倒傳遞類神經和決策樹進行插補分析。經由模擬的結果顯示，對於連續型的遺漏值，迴歸模型提供了最佳的插補估計；而類別型的遺漏值，C5.0決策樹是最佳的選擇。此外，對於資料庫中的稀少資料，當連續型的遺漏值，倒傳遞類神經模型提供了最佳的插補估計；而類別型的遺漏值，亦是C5.0決策樹是最佳的選擇。 / Data plays a vital role as source of information to the organization especially in the era of information and technology. A meaningful, qualitative and representative database if properly handled could mean a promising breakthrough to the organizations. However, from time to time, we may encounter a not so perfect database, that is we have the situation where the data in the database is missing. With the incomplete database, the results obtained from such database may provide biased or misleading solutions. Therefore, the purpose of this research is to place its emphasis on imputing missing data of the value-added database then builds the model in accordance to the type of data. If the missing data type is continuous, regression model and BPNN neural network is applied. If the missing data type is categorical, logistic regression, BPNN neural network and decision tree is chosen for the application. Our result has shown that for the continuous missing data, the regression model proved to deliver the best estimate. For the categorical missing data, C5.0 decision tree model is the chosen one. Besides, as regards the rare data missing in the database, our result has shown that for the continuous missing data, the BPNN neural network proved to deliver the best estimate. For the categorical missing data, C5.0 decision tree model is the chosen one.

2006 年新巴塞爾資本協定的施行，台灣各家銀行皆陸續步入實施新協定的軌道，發展以風險評等的觀念來計算法定適足資本，讓銀行採自建信用評等系統來評估違約暴險，並透過信用評等達到早期預警的效果，降低信用風險。而資料採礦則是近年來在應用分析領域中相當熱門的議題。 本研究是將資料採礦技術導入企業信用評等模型的建置程序，以國內某一銀行的企業授信資料為實例，資料的觀察期間為2003至2005年，其中針對「製造業」進行研究。藉由企業財務報表簽證資料，結合授信往來紀錄等變數，經誤差抽樣，分別以類神經網路、決策樹及羅吉斯迴歸等採礦技術，建立模型。模型驗證的部份，依據金管會建議的七個方向執行模型之驗證。 研究結果發現，經評估確立以1:2誤差抽樣比例下，使用羅吉斯迴歸技術建模的效果最佳。模型中財務資訊、產業特性及公司的徵審授信紀錄對於違約皆具有一定的解釋預測能力，且顯示產業特性的不同，對於違約機率的預測，有一定的影響。經驗證後，此模型即使應用到不同期間或其他實際資料，仍具有一定的穩定性與預測效力，並且通過新巴塞資本協定與金管會的各項規範，表示本研究之信用評等模型，的確能夠在銀行授信流程實務中加以應用。

新巴塞爾資本協定(Basel II)之內部評等法(IRB)的運用關鍵，在建立一個有效的信用風險模型，而此模型的功用在於將銀行的風險程度，以量化的風險因子來表達；而本研究即是針對新巴塞爾資本協定，探討在新協定的最低要求(Minimum Requirement)下，銀行欲採用內部評等法(IRB)架構信用風險系統時，應如何建立信用風險模型中「違約機率(PD)」的量化流程。 本篇研究以國內某家金融機構的資料為例，利用羅吉斯迴歸來進行製作信用評分模型，在信用評分模型正確性的指標測試中，不論是在Kolmogorov-Smirnov值、ROC比率、Gini係數的測試上，皆比原此家金融機構在正確性指標測試中更為出色。 最後，更進一步依照該模型所預測之違約機率，建立信用評等，並同時探討不同等級之客戶特性，使金融機構能更有效率地加強其風險控管，進而改善其顧客關係管理系統。