累積財富的方法隨著時代背景的不同而改變，在二十一世紀的今天，投資就是一個可以快速累積財富的方法。近年來國民所得與理財知識的提升，使得今日的台灣證劵市場交易活絡，根據台灣證劵交易所與財政部證劵暨期貨管理委員會統計資料顯示，股票市場已成為國內投資者重要的理財管道。 　　而試圖在股市或是衍生性商品中投資獲利者，不可不重視股票價格的變化。然而影響股票價格的因素極為廣泛，對於如此大量且複雜的資訊，實非一般投資人可以輕易掌握的。 近年來，藉資訊科技的快速發展，資料探勘應用於股市金融領域變的可行，優點是可以在大量的資訊裡找出有用的資訊。本研究目的在利用資料探勘的技術來尋找股票市場之買進標的與切入時機。 本研究探討單一個股的價格走向，是否會跟群體股票有所關連。利用歷史交易資料找出股票之間的股價漲跌關連度與技術指標關連度，進而發展出條件機率法則與投票法則來求出每檔股票的買進與賣出推薦值，最後再依推薦值的變化來判斷買進與賣出的標的股票。 　　本研究以2004年3月到2006年3月為訓練期間；2006年3月到2007年3月為預測時間。研究結果經由報酬率分佈分析、交易次數分析、正報酬比例分析、總獲利分析與「買進後持有」策略比較分析，顯示本研究所提出的四種預測模式中，以技術指標關連度搭配投票法則的方法最能夠有效的打敗「買進後持有」的策略。 / The method to accumulate wealth changes during different times. Making the investment is a method that can accumulate the wealth quickly in 21st century. The improvement of the national income makes today's stock market of Taiwan activate recently. From the statistical data of Taiwan Stock Exchange Corporation (TSEC), the stock market has already become important financing channel of investor. People attempting to earn profits in stock market must pay attention to the change of the stock price. However, many factors widely influence the price of the stock and make the investors hard to predict. In recent years, the fast development of computer science makes the technology of data mining applied to the stock market. The advantage of data mining is that we can find out useful information in a large amount of information. The purpose of this research is to use the technology of data mining to look for buying time and selling time of the stocks. This research investigates the correlation between a single stock and other stocks. By using the historical data of the stocks to find out the correlation between stocks, and developing the rules to calculate a prediction value, the recommendation of the buying or selling time of a stock can be done. The training analysis in this paper is collected from March, 2004 to March, 2006 and the prediction time is from March, 2006 to March, 2007. The empirical result shows that: from the distribution analysis of the profit rate, the trade number of the times analysis, the buy-and-hold policy comparative analysis, and the positive profit rate analysis: in the four models discussed the index with the rule of vote performs better than the buy-and-hold policy.

由於資訊科技的進步與網路的普及，企業得以收集與儲存大量的資料。使用資訊工具來協助資料處理、資訊擷取、以及產生知識已然變成企業的重要課題之一，所以如何良好運用資料探勘工具成為使用者關注的焦點。由於並非每一個使用者對於資料探勘的原理都有充分的了解，所以如何從探勘工具提供的功能中選用最佳的解決方案並不容易。如果對於探勘結果不滿意而需要調整軟體邏輯，與IT人員的協商溝通卻又曠日費時。 為了解決這個問題，本研究提出一個演算法選擇方法，藉由分析商業應用的內容，來自動對應到特定的資料探勘方法與演算法，讓選擇演算法的過程更為快速、更系統化，提升利用資料探勘工具解決商業問題的效率。 / Due to the information technology improvement and the growth of internet, companies are able to collect and to store huge amount of data. Using data mining technology to aid the data processing, information retrieval and knowledge generation process has become one of the critical missions to enterprise, so how to use data mining tools properly is users’ concern. Since not every user completely understand the theory of data mining, choosing the best solution from the functions which data mining tools provides is not easy. If user is not satisfied with the outcome of mining, communication with IT employees to adjust the software costs lots of time. To solve this problem, a selection model of data mining algorithms is proposed. By analyzing the content of business application, user’s requirement will map to certain data mining category and algorithm. This method makes algorithm selection faster and reasonable to improve the efficiency of applying data mining tools to solve business problems.

工作流程（workflow）是商業流程自動化的一部份。一個工作流程是由完成一件工作所有可能執行的活動（activity）以及活動間在執行時的前後關係所構成。而工作流程的設計或改進舊有的工作流程是商業上很重要的工作，因為工作流程的好與壞會影響企業的競爭力。工作流程探勘（workflow mining）是利用資料探勘的技術，分析工作流程執行時所留下的流程執行記錄，還原出一個能夠產生這些記錄的工作流程模型（workflow model），而這個工作流程模型可做為設計新模型或改進既有模型的參考。 本研究針對我們所定義的工作流程模型，以一個未知的工作流程模型所產生的流程執行記錄（workflow log）當做輸入資料（input data），提出方法利用輸入資料還原一個能夠產生輸入資料中所有資料工作流程模型，且希望這個工作流程模型能與產生流程執行記錄之未知模型越相似越好。我們提出兩個還原工作流程模型的演算法，並利用precision和recall來評估還原的模型與未知模型間的相似程度，驗證我們所提出方法的效果。實驗結果顯示，我們的方法所還原的工作流程模型precision和recall值都能達到80%以上。 / The workflow plays an important role in business process automation. A workflow is composed of activities and causal relations between activities to complete a task. Workflow design and refinement are important tasks in business process reengineering. As a workflow is executed, the orders of the executed activities are recorded in workflow logs. Workflow mining utilizes the technology of data mining to analyze these workflow logs, and reconstruct a workflow model. In this thesis, we investigate the workflow mining problem to reconstrcuct the workflow model. Two algorithms are proposed to reconstruct a workflow model. We evaluate our proposed algorithms by precision and recall to measure the similarity between the constructed and the groundtruth models. The result of the experiment shows that our proposed methods can achieve 80% precision and 80% recall for the reconstruction of workflow models.

在這個資訊快速擴張的時代，許多種類的資料庫被應用在各式各樣的領域中。空間資料探勘即是一個例子，它在空間資料庫中探勘出頻繁的樣式以及空間關係。空間資料探勘是在空間資料庫中挖掘出有趣的、以前不知道的、但實際上是有用的樣式或空間關係。 在本篇論文中，我們探勘空間序列的問題。我們主要討論兩個主題：空間相關樣式，以及空間相似相關樣式。關於空間相關樣式，我們提出以Apriori為基礎以及深度優先為基礎的解法。在空間相關相似樣式部分，我們提出兩個演算法AP-mine以及AS-mine來解決我們的問題。在AP-mine中，我們提出一個名為AP-tree的資料結構來有效率的挖掘出空間相關相似樣式。最後我們以實驗來驗證我們的演算法。 / With the growth of data, a variety of databases are applied in many applications. Spatial data mining is an example, and it discovers patterns or spatial relations from large spatial databases. Spatial data mining is the process of discovering interesting and previously unknown, but potential useful patterns or spatial relations from large spatial databases. In this thesis, we explore the problem of spatial sequential pattern mining. The two issues spatial co-relation patterns and approximate spatial co-relation patterns will be discussed. We utilize Apriori-based method and depth-first based method to solve the problem of spatial co-relation patterns. About approximate co-relation spatial patterns, we propose two algorithms, named AP-mine and AS-mine. In AP-mine, we propose a data structure, named AP-tree, to efficient mining the approximate spatial co-relation patterns. Lastly, We also perform the experiments to evaluate our spatial co-relation pattern mining algorithms.

電腦音樂作曲一直是電腦音樂研究者的夢想。本論文中，我們要探討的是從給定的多首音樂中，利用音樂作曲規則探勘的方式，找出給定音樂中的樣式，以產生具備這些音樂風格的新音樂。我們針對，音樂結構、和弦風格和音樂動機三項音樂特性做分析與探勘。在音樂結構部分，我們利用資料探勘技術分析音樂結構，並且學習音樂結構的特性。在旋律風格部分，我們分析每首音樂旋律的和弦以做為旋律的特徵，並從中探勘音樂的旋律風格。音樂動機部份，我們探勘出音樂動機，並探勘出音樂中音樂動機的重要性，以建立音樂動機挑選模型。最後根據音樂結構、和弦、音樂動機，三項音樂特性的學習結果產生整首音樂。在效果評估方面，我們採用類似Turing Test的方式，以測試機器產生的音樂和人所作曲的音樂之辨別率。結果顯示產生的音樂和人所作曲的音樂不易分辨。另外，實驗也顯示系統所產生的音樂在旋律及和弦上接近給定的音樂風格。 / Computer music composition has been the dream of the computer music researcher. In this thesis, we investigate the approach to discover the rules of music composition from given music objects, and automatically generate a new music object style similar to the given music objects. To discover the rules of music composition, the music is analyzed by addressing three music properties, music structure, melody style and motif. We exploit the data mining techniques to analyze music structure. For the melody style, chord is utilized to represent the feature of melody and melody style is discovered from chords of music objects. For the motif, modified repeating pattern finding algorithm is employed to discover the motives. Then, the motif selection model is constructed. A new music object is generated based on the discovered rules in terms of three music properties. To measure the effectiveness of proposed computer music composition approach, we adopt the method similar to the Turing test to test the discrimination between machine-generated and human-composed music. The result showed that it is hard to discriminate. Another experiment showed that the style of generated music is similar to the given music objects.

近年來越來越多人基於健康理由，自己動手烹調料理，也帶動食譜社群網站的成長。雖然隨著Big Data議題受到注目，Data Mining在近年來相當熱門，然而針對食譜的巨量資料探勘與分析研究並不多。 本研究由網路擷取國外知名食譜網站Allrecipes.com、Food.com及Yummly.com的食譜資料，探勘世界主要料理的食材樣式與特性，包括料理口味、常用食材、特色食材、核心食材、食材搭配關係、料理間相似度與分群、及料理自動分類。 針對資料前處理，本論文提出結合食材詞庫並利用連通單元標籤演算法，提出解決食材同義詞的方法。為了探勘料理的食材樣式與特性，本研究透過網絡分析、關連規則、Phi, PMI等方法來探勘分析各種料理的特色食材、核心食材與食材搭配樣式。此外，本論文依據料理食材之相似度，並結合階層式分群技術，有別於一般以地理位置來群聚各類料理。本論文也提出運用階層式分類技術，以根據食材來自動判斷食譜的料理種類。 透過食譜網站的大量的使用者產生資料，探勘分析世界各種料理的樣式與特性，將可了解各種料理的風格與特色，進而應用在食譜網站的資料管理與查詢。

本研究以台灣50與中型100成分股為對象，運用資料探勘特徵選取技術，分析影響股票完全填權息成功之關鍵因素，並依此關鍵因素建構一個完全填權息預測模型，最後比較研究結果與過去研究之異同。本研究完全填權息預測模型的建構過程分為五階段：(1)定義完全填權息之股票：運用TEJ資料庫抓到的歷史股價資料與股利資訊，計算除權息前與除權息後股價，標註完全填權息和未完全填權息二個類別。(2)影響填權息相關因素：根據過去文獻所發現，影響短期填權息行情超額報酬的因素，以及影響股價的基本面因素，蒐集與股利相關的指標與基本分析中所用的公開財務報表資料。(3)特徵選取分析：利用循序前進搜尋（SFS）結合分類演算法，整合與計算所有影響因素資料，藉此找出關鍵的影響因素。(4)預測模型建立：根據特徵選取之結果資料，使用Weka軟體進行資料探勘支持向量機和決策樹分類模型訓練。(5)模型準確性比較與分析：本研究所建構之模型可協助存股型投資者，判斷可領取高股息且無股價損失之股票，提供投資人選股參考。 / In this study, we use the Feature Selection Method for Data Mining to analyze the key factors that may affect the rate of the stock price successfully remaining previous price after dividend payment among stocks of 50 largest companies and 100 medium-sized companies in Taiwan. Based on these key factors, we construct a forecasting model for stocks with the 100% flat stock price. Finally, We try to find out the similarities and differences between the current study and past research. In this study, the construction of a forecasting model for stocks with the 100% flat stock price is divided into five stages: (1) Defining stocks with the 100% flat stock price: Marking stocks with the 100% flat stock price and the non-100% flat stock price on historical stock data and dividend information captured by the TEJ database; (2) Relevant Factors Affecting increase in the stock price after dividend payment: According to the factors found in the past literature that may affect excess returns from short-term increase in the stock price after dividend payment and the fundamental factors affecting the stock price, we are able to collect indexes related to dividends and public financial statements for basic analysis. (3) Feature Selection Analysis: By using the Sequential Forward Selection (SFS) method and the classification algorithm, all influencing factors are integrated and calculated to find out the key influencing factors; (4) The Establishment of the Prediction Model: According to the results of feature selection, we use the Weka software to conduct data mining and train the classification model based on support vector machines and decision trees. (5) Comparison and Analysis on Accuracy of the Model: The model constructed in this study can help stock-holding investors determine stocks with high dividends without loss of the stock price and provide reference for investors in stock selection.

現在有許多對於眼動軌跡與人在認知方面的研究，包括理解狀態以及感興趣的程度；其中，閱讀文章時的眼動軌跡是最常被討論及研究的題材。而本研究的目的就是希望探討讀者在閱讀時的眼動軌跡，與其感興趣程度之間是否存在關係。 / 本研究的特色在於，我們不用一般分析眼動時關心每個AOI（area of interest）上的眼動資料，而是希望將眼動資料以序列的方式分析，並且運用資料探勘的方法，找出眼動序列中區分感興趣程度的眼動軌跡特徵的片段。 / 透過對於眼動軌跡的分析，我們希望研究的結果，在未來可以運用在資訊檢索的領域上，成為一種有效的「隱含式回饋（implicit feedback）」的方式，以改善現有資訊檢索效能。 / Much research has been performed on the relationship between eye movements and human cognition, including comprehension and interesting degree. The purpose of our research is to find out if there are relationships between eye movements of reading and interesting degree. / Instead of analyzing the eye movements on each area of interest, the characteristic of our research is to transform eye movements to sequence data, and to determine the eye movement patterns which discriminate whether user is interesting or not by using the method of data mining. / Through the analysis of the eye movements, our research result can be used as one way of implicit feedback of information retrieval to improve the effectiveness of the search engine.

空間認知(Spatial Cognition)指出人所理解的空間複雜度，也就是人與環境互動的過程中，經由記憶與感官經驗，透過內化與重建產生物體在空間的關係認知。認知圖(Cognitive Map)是最常被使用在評估空間認知。分析學生所畫的認知圖有助於老師們瞭解學生的空間認知能力，進而擬定適當的地理教學設計。我們視空間認知發展的學習歷程檔案是由這些認知圖所構成。隨著數位學習科技的進步，我們可以透過探勘認知圖的方式，探討空間認知發展的學習歷程檔案。因此，我們藉由透過圖像的空間資料探勘，分析學生空間認知發展的學習歷程。 空間資料探勘(Spatial Data Mining)主要是從空間資料庫或圖像資料庫中找出有趣且有意義的樣式。在論文中，我們介紹一種空間樣式(Spatial Co-orientation Pattern)探勘以提供空間認知發展學習歷程的分析。Spatial Co-orientation Pattern是指圖像資料庫中，具有共同相對方向關係的物體(Object)常一起出現。例如，我們可以從圖像資料庫中發現物體P常出現在物體Q的左邊，我們利用二維字串(2D String)來表示物體分佈在圖像中的空間方向關係。我們透過Pattern-growth的方法探勘此種空間樣式，藉由實驗結果呈現Pattern-growth的方法與過去Apriori-based的方法[14]之優缺點。 我們延伸Spatial Co-orientation Pattern的概念至時空資料庫(Spatio-temporal Database)，提出從時空資料庫中，探勘Temporal Co-orientation Pattern。Temporal Co-orientation Pattern是指Spatial Co-orientation Pattern隨著時間的變化。論文中，我們提出兩種此類樣式，即是Coarse Temporal Co-orientation Pattern與Fine Temporal Co-orientation Pattern。針對此兩種樣式，我們提出三階段(three-stage)演算法，透過實驗分析演算法的效率。 / Spatial cognition means how human interpret spatial complexity. Cognitive maps are mostly used to test the spatial cognition. Analyzing cognitive maps drawn by students is helpful for teachers to understand students’ spatial cognitive ability and to draft geography teaching plans. Cognitive maps constitute the portfolios of spatial cognitive development. With the advance of e-learning technology, we can analyze portfolios of spatial cognitive development by spatial data mining of cognitive images. Therefore, we can analyze portfolios of spatial cognitive development by spatial data mining of images. Spatial data mining is an important task to discover interesting and meaningful patterns from spatial or image databases. In this thesis, we investigate the spatial co-orientation patterns for analyzing portfolios of spatial cognitive development. Spatial co-orientation patterns refer to objects that frequently occur with the same spatial orientation, e.g. left, right, below, etc., among images. For example, an object P is frequently left to an object Q among images. We utilize the data structure, 2D string, to represent the spatial orientation of objects. We propose the pattern-growth approach for mining co-orientation patterns. An experimental evaluation with synthetic datasets shows the advantages and disadvantages between pattern-growth approach and Apriori-based approach proposed by Huang [14]. Moreover, we extend the concept of spatial co-orientation pattern to that of temporal patterns. Temporal co-orientation patterns refer to the change of spatial co-orientation patterns over time. Two temporal patterns, the coarse temporal co-orientation patterns and fine temporal co-orientation patterns are introduced to be extracted from spatio-temporal databases. We propose the three-stage algorithms, CTPMiner and FTPMiner, for mining coarse and fine temporal co-orientation patterns, respectively. An experimental evaluation with synthetic datasets shows the performance of these algorithms.

電子商務，這是在90年代才興起的經營模式。在過去的社會演進裡，人類從最早的農業經濟進步為製造經濟，一切以產品、品質為出發；隨著知識時代的來臨，製造不再只是一味大量生產，更為重要的是站在顧客角度思考，而慢慢從製造經濟轉變為服務經濟，以顧客的滿意為重。 以滿足顧客的需求來看，網路商店必須具備高效率、可授權的、動態的且反應速度快的特性。消費者需要個人化資訊來做決定，然而，這似乎都是現有電子商務網站所欠缺的。 本研究將會採用資料探勘、灰關聯分析、層級分析法與灰色預測來達成客製化的行銷策略、產生客觀的產品排名與客製化的產品排名，並加以預測客戶的喜好。 / E-commerce, it’s a new business model from 90’s. On the social evolution track, from agriculture economy to manufacture economy, product and quality is the spotlight. With the coming of knowledge era, manufacturing is not just mass-productive, this era’s spotlight is the customer satisfaction. The evolution track had moved from manufacture economy to service-oriented economy. If sellers want to meet customers’ need, it should had some features, such as efficient, empower, dynamic, quick response, and so forth. Customers need tailor-made information to make the purchasing decision. However, nowadays internet stores cannot meet this need. This research will utilize data-mining, grey relation analysis, analysis hierarchy process and grey perdition to draw up tailor-made marketing strategies, generating objective product ranking and tailor-mage product ranking, and predict customers’ preference trend.