我國目前針對資料挖掘之研究，尚處於萌芽時期，相關之研究、期刊與商業文章等，散見於學術論文及報章間，但針對資料挖掘於管理面及企業實際導入層面的研究目前相當匱乏。因此本研究嘗試從管理層面來探討企業如何導入資料挖掘，並藉由深度訪談個案公司的方式，描述我國目前的應用情況。 根據前述本研究之研究動機，故本研究希望能藉由國內外相關文獻探討，與對國內銀行業者之實際訪談，來瞭解目前國內實施資料挖掘之作法與影響，以及在此過程中所遭遇到的困難。資料挖掘導入企業，其重點應在於企業領域方面的知識，要結合企業中使用者的語言和分析過程，才能發揮工具的效能與增進企業的智慧，本研究旨在回答下列問題： 1.企業本身資源如何影響導入資料挖掘的決策與實施方式。 2.企業導入資料挖掘時，在組織結構上的變化如何，組織的作業流程產生的變化為何。 3.以台灣企業來說，目前資料挖掘的應用領域為何。 目前我國銀行業在資料挖掘的情況大多在起步階段，有少部分公司在資料倉儲建置之前，已經以土法煉鋼的方式，使用套裝軟體進行小範圍的資料挖掘試驗，大部分仍不敢貿然採用資料挖掘。 由於資料挖掘對於企業來說是一種新技術也是一種新觀念，因此大部分都會特別成立一個單位，專門負責資料挖掘的工作，除了支援傳統上的行銷單位，例如信用卡行銷單位、行銷企劃單位外，並協助新興的電子商務部門，對網站資料加以分析。 企業會基於資料挖掘的應用範圍與財力的因素，考量是否討入外部顧問，或自行建置。若是資料挖掘專案的範圍小，或僅做為初步的資料分佈的分析，則以自行建置的方式為主，因為這樣成本比較低。

近年來雲端運算技術的發展與電腦設備效能提升，使得以大量電腦主機以水 平擴充的方式組成叢集運算系統，成為一可行的選擇。Apache Hadoop 是Apache 基金會的一個開源軟體框架，它是由Google 公司的MapReduce 與Google 檔案 系統實作成的分布式系統，可以管理數千台以上的電腦群集。Hadoop 利用分散 式檔案系統HDFS 可以提供PB 級以上的資料存放空間，透過MapReduce 框架 可以將應用程式分割成小工作分散到叢集中的運算節點上執行。 此外，企業累積了巨量的資料，如何處理與分析這些結構化或者是非結構化 的資料成了現在熱門研究的議題。因此傳統的資料挖掘方式與演算法必須因應新 的雲端運算技術與分散式框架的概念，進行調整與改良，發展新的方法。 關聯規則是分析資料庫龐大的資料中，項目之間隱含的關聯，常見的應用為 購物籃分析。一般情形下會在特定的維度與特定的顆粒度範圍內挖掘關聯規則， 但這樣的方式無法找出更細微範圍下之規則，例如挖掘一個年度的交易資料無法 發現消費者在聖誕節為了慶祝而購買的商品項目間的規則，但若將時間限縮在 12 月份即可挖掘出這些規則。 Apriori 演算法是挖掘關聯規則的一個著名的演算法，透過產生候選項目集 合與使用自訂的最小支持度進行篩選，產生高頻項目集合，接著以最小信賴度篩 選獲得關聯規則的結果。若有k 種單一項目集合，則候選項目集合最多有2𝑘 − 1 個，計算高頻項目時則需反覆掃描整個資料庫，Apriori 這兩個主要步驟需要耗費 相當大量的運算能力。 因此本研究將資料庫分割成多個資料區塊挖掘關聯規則，再將結果逐步更新 的演算法，解決大範圍挖掘遺失關聯規則的問題，結合spark 分散式運算的架構 實作程式，在電腦群集上平行運算減少關聯規則的挖掘時間。 / Because of improving technique of cloud-computing and increasing capability of computer equipment, it is feasible to use clusters of computers by horizon scalable a lot of computers. Apache Hadoop is an open-source software of Apache. It allows the management of cluster resource, a distributed storage system named Hadoop Distributed File System (HDFS), and a parallel processing technique called MapReduce. Enterprises have accumulated a huge amount of data. It is a hot issue to process and analyze these structured or unstructured data. Traditional methods and algorithms of data mining must make adjustments and improvement to new cloud computing technology and concept of decentralized framework. Association rules is the relations of items from large database. In general, we find association rules in fixed dimensions and granular database. However, it might loss infrequent association rules. Apriori algorithm is one famous algorithm of mining association rule. There are two main steps in this algorithm spend a lot of computing resource. To generate Candidate itemset has quantity 2𝑘 − 1, if there are k different item. Second step is to find frequent, this step must compare all tractions in the database. This approach divides database to segmentations and finds association rules of these segmentations. Then, we combine rules of segmentations. It can solve the problem of missing infrequent itemset. In addition, we implement this method in Spark and reduce the time of computing.

以往顧客服務對企業而言只是被動的支援單位，企業無法得知每一位顧客的想法，顧客的聲音也難以傳達到企業內部，然而資訊與通訊技術的進步打破了這樣的障礙，促使資訊透明化，企業藉由資訊科技的輔助可以直接接觸到每一個個別的顧客，了解顧客的想法與需求來帶動企業的運作，成為企業經營的競爭優勢。 顧客關係管理（customer relationship management；CRM）為近年新興熱門話題，國內外軟體大廠紛紛投入CRM市場，然而此環境尚未成熟，各家提出之系統功能相當不一致，本文提出企業在建置顧客關係管理環境時應具備七大單元，包括產品端資訊蒐集機制、互動機制、事件處理機制、儲存分析機制、內部鏈結機制、策略對應機制、回饋執行機制等，以供企業參考。 CRM重視區別出每一個個別顧客的屬性再提供客製化服務，本文針對北部某醫院進行訪談，探討其規劃中之CRM及進行狀況，驗證本文所提出模式之可行性。 / Because of the power of information and telecommunications technologies, business can keep tracking of their customers to know what they really want and how they actually use the product. Analyzing the information return from customers and products, business can provide active and accurate service to the right customer through the right channel at the right time and rise the customer satisfaction. 　The purpose of this article is to show a complete CRM model which includes product information collecting mechanism, interact mechanism, event processing mechanism, storage and analysis mechanism, internal linkage mechanism, decision support mechanism, feedback implementing mechanism. When business constructs the CRM environment, they can apply this model to their organization. 　This article also studies a hospital case and plans a future framework for it by applying the model.

隨著資訊時代的來臨，網際網路用戶數的急速成長，客戶資料大量湧入的結果造成了企業回應客戶的困難；此外，入口網站的日漸普及與市場新利基的建立，使得上網者對專業化網站的需求提高；而配合新行銷時代的來臨，新科技行銷已從以往的大眾行銷走向一對一的個人化行銷。在這樣的背景與動機驅動下，近年來資料倉儲技術的興起，在這個講求競爭及速度的時代，提供了一套真正能創造企業優勢的方法，資料倉儲與客戶關係管理也逐漸成為企業發展與競爭不可或缺之工具。 　本研究乃是利用資料倉儲等相關技術，期望能達到網路市場區隔之目的，進而提供行銷相關之建議。因此，研究中定義了入口網站之會員特性分析模式，從中發掘出會員特性模式之分析結果，再配合入口網站功能階段分類表的服務內容，與研究中所訂之行銷模式，修改並制訂最後行銷策略上之參考建議。 　目前國內在資料挖掘上之相關研究，乃以金融、銀行、保險等領域的應用較多，對於入口網站此領域的應用尚無相關研究出現。另外，在網際網路發達，且入口網站百家爭鳴的背景之下，如何提高會員註冊率，與如何保留住已註冊的會員，是網站經營者所需考量的兩個重要課題。有鑑於此，本研究期望帶給網路業者一個新的思考方向與啟發，也試著將資料挖掘的技術帶入網際網路的應用領域，由網站會員特性的初步分析開始，做一簡單之示範。 / With the approach of informational era, the Internet users grow rapidly and the mass production of customers' data results in the difficulty of the response from industries to customers. Moreover, the widespread use of portal sites and the establishment of new market opportunity let the Internet users raise their demand to specialized websites. Because of the coming of new selling era, the selling of new science and technology has changed from the former mass selling to one-to one individual selling. Under the drive of this kind of background and motive, Data Mining technology rises and develops recently. In the era that emphasizes competition and speed, we provide a method that creates the superiority of industries. Data Warehouse and Customer Relationship Management (CRM) is becoming the essential tool for the development and competition of the industries. 　This research uses some technologies related to Data Warehouse in order to segment the Internet market, and provide the related selling suggestions. Therefore, the research defines the analytic model of characteristics of members of portal sites, discovers the analytic results, and then to cooperate with the services mentioned in the classified table of functions/stages of the portal site and the selling model made in the research, and then modifies and makes the final suggestions on selling tactics. At present, the internal researches about Data Mining are usually applied in the domains of finance, banking, and insurance, not in the domain of the portal sites. Besides, in the situation of prosperous Internet and competition of portal sites, how to raise the register rate of members and how to keep the registered members are the two important topics considered by website managers. Consequently, this research hopes to bring not only a new direction and inspiration to managers, but also a new applied domain, Internet, for Data Mining. This research will begin with a simple example that analyzes the characteristics of a Portal Sit's Members and the Making of Selling Tactics.

處在變化快速的網路環境中，入口網站如何建立起專屬的會員制度，以期行銷人員能在大量的會員資料庫中找出有用的資訊，掌握會員的網路行為模式、實現個人化之服務、有效區隔市場及瞭解不同會員之網路行為模式等，進而以制定適當之行銷策略而達成結合實體行銷之目標。而資料挖掘的技術能在資料量龐大的會員交易資料庫中，利用會員的基本資料與交易資料衍生建立相關的評估指標，以評估會員的特質、需求模型、消費特徵、建立市場區隔的行銷策略等，行銷人員藉此可採用不同的宣傳方式與促銷策略，以達最佳的獲利結果。 本研究以國內某入口網站真實之會員基本資料及入口網站之商品：BBS頻道與財經頻道的資料檔，做為會員網路行為模式之資料分析的基礎。本研究利用資料挖掘的技術，找出入口網站的會員與商品之分群特徵，並發掘會員在兩頻道間的網路行為的關聯規則。另一方面，本研究利用關聯規則演算法，考量實際在發掘關聯規則分析所碰到的問題，實作出一套操作流程式較為簡便的關聯規則分析程式。本研究提供不同的關聯規則分析角度，以考量會員購買商品項目組合的關聯規則，進而支援決策者制定相關商品的促銷決策，以提高銷售量。 / In the rapid-changing network environment, how do Portal Sites establish exclusive membership mechanism in order to filter useful information out of their own database, master the network behavior models of their members, realize personalized services, and effectively segment and understand different network behavior models of all members? However, data mining can use the basic members' information and transaction data to produce the associated evaluation indicator during the high volume transaction database in order to evaluate the customers’ traits, demand models, consuming characteristics, and establish the marketing strategy of segmenting target market. As a result, we can adopt different advertising types and promotion strategies to achieve the best profitable goals. The research is based on the real data of members and the merchandise of some website in Taiwan. (i.e. using data files of BBS channel and financial channel as the fundamental analysis data of network behavior models). Per using the data mining techniques, we can not only find out the characteristics of the members of portal sites and the clustering of merchandises, but unearth the association rules of network behavior of the two kinds of channels. On the other hand, this research, according to the association-ruled calculation method, is considering the practical problems when excavating association-ruled analysis methodology and producing a much simpler association-ruled analysis program. By providing the list of best buyers, the association-ruled analysis program will consider the association rule of members’ buying component and for further step, support the decision-maker to institute the related promotion strategy in order to raise the sales volume.

在現今資訊潮流中，企業的龐大資料庫可藉由統計及人工智慧的科學技術尋找出有價值的隱藏事件。利用資料做深入分析，找出其中的知識，並根據企業的問題，建立不同的模型，進而提供企業進行決策時的參考依據。資料挖掘的工作是近年來資料庫應用領域中相當熱門的議題。它雖是個神奇又時髦的技術，卻不是一門創新的學問。美國政府在第二次世界大戰前，就於人口普查以及軍事方面使用資料挖掘的分析方法。隨著資訊科技的進展，新工具的出現，以及網路通訊技術的發展，常常能超越歸納範圍的關係來執行資料挖掘，而由資料堆中挖掘寶藏，使資料挖掘成為企業智慧的一部份。在本篇論文當中，將資料挖掘技術中的關聯法則 ( Association Rule ) 運用至房地產的價格分析，進而提供有效的關聯法則，對於複雜之房價與週邊環境因素作一整合探討。購屋者將有一適當依循的投資計畫，房產業者亦可針對適當的族群做出適當的銷售企畫。 / At this technological stream of time, it is able to extract the value of corporations’ large data sets by applying the knowledge of statistics and the scientific techniques from artificial intelligence. Through the use of these algorithms, the database will be analyzed and its knowledge will be generated. In addition to these, data models will be sorted by different corporation issues resulting in the reference for any strategic decision processes. More advantages are the predictions of future events and how much public is willing to contribute and feedback to new products or promotions. The probability of outcomes will be helpful as references since this information is referable to ensure companies providing quality services at the right time. In another words, companies will have clues in attempts to understand and familiarize their customers’ needs, wants and behaviors, as a result of delivering best services for customers’ satisfactions. Data mining is such a new knowledge that is commonly discussed in the field of database applications. Although it is a relatively new term, the technology is not exactly due to the analysis methods used. Before World War II, the analysis techniques were used in particular to the statistics in census or cases related to military affairs by the US government. Knowledge discovery has been one part of business intelligence in current corporations because these new techniques are inherently geared towards explicit information, rather than just simple analysis. By applying association rules from knowledge discovery technology, this dissertation will provide a discussion of price estimation in real estates. This discussion is involved in investigations into diverse housing prices resulting from the factors of surrounding environment. By referring to this association rule, buyers will acquire information about investment plans while housing agents will gain knowledge for their plans or projects in particular to their target markets.

各國政府為提高國際競爭優勢，紛紛積極推動「電子化政府」。我國電子化政府建設自八十六年起開始推動，迄今已經行政院擴大為e-Taiwan計畫。電子化政府推動之業務電腦化，帶動政府業務資訊系統的快速發展，其彙集而成之大型資料庫，為政府統計工作帶來莫大的發展契機。 本研究從電子化政府的過程、內政業務行政程序、知識挖掘及採勘方法，提出參考資料模型，可能的統計軟體工具以及電子化政府中知識發現的實驗架構。再者，本研究藉臺閩地區外籍與大陸配偶結婚登記資料集，運用各種群集分析如K-means、ANN、TwoStep等，並利用我國人口數時間序列採用多模式方法進行人口預測，並將前述分析結果回饋資料庫，最後，作者實現一個知識發現系統雛型，其中包含了前端資料庫、資料集、知識庫以及EIS使用介面。 本研究成果總結如下：(1)資料挖掘工作產出之知識，除真實呈現社會現象外，亦作為政府政策之指南；(2)在本研究發展之系統中，新興資料挖掘技術及傳統資料分析方法，二者相輔相成；(3)某些資料挖掘技術適合相符的資料型態，例如文中人口預測資料較適合指數平滑法勝於ANN，亦即，我們可以籍由多模式分析比較其結果，來達到更佳的效果；(4)藉由知識庫模型的建立達成知識創造、共享與管理的目標；(5)資料挖掘工作可以回饋改善資訊系統或業務缺失。 / In order to enhance international competitive advantages, most of the government authorities over the world are engaging in realizing their e-Government platforms. The ROC Government began to develope its e-Government- Infrastructure since 1997, and up-to-date is expanding the e-Taiwan Project as a whole by Executive Yuan. The computerization of administration processes within various government agencies push forward fast development of administration information systems via handling administrative works and lead to utmost opportunities for the government statistics by means of very large databases. Starting from a survey on developements of e-Government, administrative processes for interior affairs, and knowledge mining as well as discovery techniques, this study brings out reference data models, potential statistical softwaretools, and an experimental framework as a whole for knowledge discovery in the context of e-Government. In the next step, this study experiments with applying clustering techniques such as K-means, ANN, and Twostep on datamart regarding marriage of foreigners ( including citizens from Mainland China ) in Taiwan, and with employeeing multi-modes approach on population forecasting. The results of aforementioned analysises are feed into backend database. At last, this author carries out a prototype of knowledge discovery system which includes front-end data base, data marts, knowledge base and interfaces to EIS. The results of the research can be summarized as following: 1.Knowledge derived by means of data mining is capable to represent social events / affairs as well as to serve as a kind of guideline for developing government ploicies. 2. The modern data-ming techniques and classical data-analysis approaches complement with each other in the system developed in this research. 3. Certain mining technique is suitable of corresponding data pattern, for example, expotential smoothing is more suitable for our population data than ANN, which means that we may often achieve better result by multi-mode analysis and comprison with the outputs of different modes. 4. Knowledge creation, sharing, and management can be achieved by means of the knowledge discovery processes on the framework developed in this research. 5. We can figure out errorful raw data in the mining output and feedback to the data source to improve its quality.

客戶在從事消費時，往往會有許多不一樣的行為產生。對組織而言，研究客戶的消費行為能夠協助組織更了解客戶的資訊，進而支援其經營活動。以往與客戶行為相關的資料挖掘研究，較著重於客戶的消費資料。而對於客戶在商店中做了那些動作，及其動作會導致發生的事件並沒有較全盤及深入的討論。對實體業者而言，要實際的去記錄使用者在商店內的行為，是不太可行的；但相對的說，隨著網際網路與資料收集技術的發展，網站經營者應用log留存技術，將比傳統業者更容易且完整的收集到消費者行為記錄。本研究試圖在全球資訊網的環境中建立一個能夠同時分析使用者的瀏覽網頁路徑及其動作過程的演算法；並且配合該演算法建置一個雛形系統，以驗証其效能，最後並評估其日後實務操作的可行性。 / Different kind of customer purchases with different behavior. Studying the customer’s purchase behavior can help organizations understand their client intentions to support their business activities. In the past, customer behavior data mining emphasized on their purchase items, i.e., what they buy. There was few studies discussing what path they took and what actions they made in an e-store. It is impossible for a physical store to record its customers’ all actions and passing paths. However, a website store can easily collect such data in an Internet log. This study proposes a data mining algorithm that can analyze both customers’ browsing pages and their actions path. The algorithm’s efficiency and feasibility were examined in our prototype. This study may contribute to help the website mangers to restructure their website layouts or advertisement position to catch the customer’s eyes.