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研究生： 謝百恩
Shie, Bai-En
論文名稱： 於不同環境下之交易資料庫高效益探勘技術
Mining High Utility Patterns from Transactional Databases under Different Environments
指導教授： 曾新穆
Tseng, Vincent S.
學位類別： 博士
Doctor
系所名稱： 電機資訊學院 - 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年： 2013
畢業學年度： 101
語文別： 英文
論文頁數： 135
中文關鍵詞： 高效益樣式探勘 、交易資料庫 、資料串流 、行動交易環境 、興趣樣式
外文關鍵詞： Utility pattern mining, transaction database, data stream, mobile commerce environment, interesting pattern
相關次數： 點閱：145  下載：4 
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    • 資料探勘是從資料庫中找出潛在、未知且有用的資訊的一門學問，其中頻繁樣式探勘在許多資料探勘的應用中是一種常見的基礎技術。目前已有許多既有研究成功地將頻繁樣式探勘應用在不同領域。在商業領域中，從交易資料庫中找出頻繁樣式亦即在交易資料中找出經常被顧客同時購買的商品組合。然而頻繁樣式探勘的架構並未考慮交易環境中兩個相當重要的因素：商品價格與購買數量，故頻繁樣式探勘並無法滿足對找出與「利潤」相關的樣式（如高獲益商品組合）感興趣的使用者。因此同時考慮了商品價格及銷售數量的高效益樣式探勘，在資料探勘領域中成為了一個相當有應用價值的新興領域。在商業領域中應用高效益樣式探勘，找出真正最賺錢的商品組合，可協助決策者做出更精準的商業決策。然而在真實交易環境下，除了傳統的靜態交易資料庫之外，不同環境亦會產生不同類型的交易資料，如新興的兩種資料類型：串流型資料與行動交易資料，皆是最近的熱門研究議題。故本論文主要分為三個研究主題，結合高效益樣式探勘及相關探勘技術，在上述三種不同環境的交易資料庫找出符合使用者需求的結果。
    目前已有許多在傳統靜態交易資料庫使用高效益樣式探勘的論文。由於向下封閉性質在高效益探勘並無法適用，故先前研究大多使用兩階段式的探勘架構：於第一階段使用估計效益值，先找出可能為高效益的候選樣式，在第二階段再計算所有候選樣式的實際效益值，以確保輸出真正的高效益樣式。然而此架構的瓶頸在於第一階段過於高估估計效益值，使得太多的候選樣式需要在第二階段被檢查，探勘流程的效能也因此大大降低。因此在此環境下，本論文制訂能有效地降低估計效益值的策略以及設計能有效運用此策略的演算法UP-Growth與UP-Growth+，在第一階段提出新的估計效益值計算方式，有效率地過濾掉許多不必要的候選樣式，可大幅減少探勘流程所需的時間，達到有效增進探勘效能的目標。
    對大型企業而言，無時無刻都在產生巨量的交易資料，在無法儲存如此巨量資料的情境下，便造就了串流型資料的分析需求。串流資料反應了該環境的即時狀況，是一種相當重要的資料類型，因此串流資料探勘在許多研究中已被廣泛地討論。串流資料探勘的重要特性為資料快速流過，無法掃描第二次資料，故需要掃描兩次以上資料庫的傳統演算法並無法符合此需求。因此在本論文的第二個研究主題中，我們提出了名為GUIDE的架構，只需掃描一次資料，即可將所需的資訊快速地儲存在資料結構中做有效運用。此外考慮到若輸出的樣式數量過多，會讓使用者面臨無法篩選及應用這些樣式的困境。故我們結合了高效益及最大樣式探勘，在串流資料的界標、滑動型視窗及定期衰退三種模式下，找出最大高效益樣式，有效減少輸出樣式數量，達到精簡的效果。
    行動裝置已是目前最被廣泛使用的資訊產品之一，相關的應用領域與學術研究也變得愈來愈熱門。在此熱潮下，行動交易環境下的使用者行為樣式探勘也成為了新興的熱門研究主題。本論文的第三個研究主題結合了使用者的移動路徑及交易行為，從行動交易環境中找出高效益行動循序樣式。且為了讓使用者能表達個人的喜好需求，我們設計讓使用者可依個人喜好輸入之限制條件。本論文所提出的IM-Span演算法採用分治式的子資料庫遞迴探勘，將限制導入有限狀態機，只須掃描原始資料庫一次，即可找出使用者感興趣的高效益行動循序樣式。如此不僅可增進探勘效能，更可進一步地符合行動使用者的需求，達到輸出樣式個人化、減少樣式數量及增進探勘效能的效果。

    Data mining is the process of revealing non-trivial, previously unknown and potentially useful information from large databases. Extensive studies have addressed frequent pattern mining and successfully employed it in wide applications. In business domain, mining frequent patterns from transaction databases refers to discovery of itemsets frequently bought together in the transactions. However two important factors, namely unit profit and purchased quantity, are not considered in this framework. Hence, utility mining emerges as an important topic in the data mining field since it considers unit profit and sales quantity and discovers valuable utility patterns from transactional data. However, in real world applications, different environments of transaction databases make the concepts and features of data different. In this dissertation, we address the issues about mining utility patterns from different environments of transaction databases, including traditional transaction databases, transactional data streams and mobile transaction sequence databases.
    In recent years, a number of researches have addressed the topic about utility mining from traditional transaction databases. Since downward closure property is not maintained in utility mining, most of them apply the two-phase framework: finding possible high utility patterns in phase I by estimating the upper bound of their utilities and then calculating their actual utility values to ensure the real high utility patterns in phase II. Nevertheless, the bottleneck of this framework is that estimated utilities of itemsets are much overestimated so that large numbers of candidates need to be checked in phase II. This results in poor performance in mining process. In view of this, for the first part of this dissertation, we design two algorithms named UP-Growth and UP-Growth+ with strategies for lowering the estimated utilities during the mining process. With the strategies, the number of candidates is effectively reduced such that the mining performance is significantly improved.
    In many applications, huge amount of data are generated in fast and continuous way, which are considered as data streams. The issues about data stream mining are diversely discussed since data streams reflect critical real-time situations for circumstances. Therefore, in the second part of this dissertation, we propose a one-pass framework named GUIDE to discover the compact utility patterns, named maximal high utility itemsets, by integrating the concepts of utility patterns and maximal patterns. The proposed framework not only generates the patterns efficiently but also fits to three data stream models: landmark, sliding window and time fading models.
    Mobile devices are getting popular for various applications such that mining user behavior patterns from mobile environments emerges as a novel research issue. In the third part of this dissertation, we aim at discovering high utility mobile sequential patterns from mobile commerce environments. Moreover, we incorporate user-constraints for presenting users’ interests and design an algorithm named IM-Span to find interesting high utility mobile sequential patterns that meet the constraints. Finding patterns by pushing constraints into the mining process turns out to be more useful, personalized and insightful to users since it minimizes the number of resultant patterns and improves the mining performance.

    摘要 I ABSTRACT III 誌謝 V LIST OF FIGURES IX LIST OF TABLES XI CHAPTER 1 INTRODUCTION 1 1.1 MOTIVATION 1 1.2 OVERVIEW OF THE DISSERTATION 3 1.2.1 Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases 3 1.2.2 Efficient Algorithms for Mining Maximal High Utility Itemsets from Data Streams with Different Models 4 1.2.3 Mining Interesting High Utility User Behavior Patterns in Mobile Commerce Environments 5 1.3 ORGANIZATION OF THE DISSERTATION 6 CHAPTER 2 BACKGROUND AND RELATED WORK 7 2.1 FREQUENT PATTERN MINING 7 2.2 UTILITY MINING 8 2.3 MOBILITY PATTERN MINING 9 2.4 DATA STREAM MINING 11 2.5 CONSTRAINT MINING 12 CHAPTER 3 EFFICIENT ALGORITHMS FOR MINING HIGH UTILITY ITEMSETS FROM TRANSACTIONAL DATABASES 13 3.1 INTRODUCTION 13 3.2 PRELIMINARY 15 3.3 THE PROPOSED METHODS 17 3.3.1 The Proposed Data Structure: UP-Tree 19 3.3.2 The Proposed Mining Method: UP-Growth 26 3.3.3 An Improved Mining Method: UP-Growth+ 34 3.3.4 Efficiently Identify High Utility Itemsets 39 3.4 EXPERIMENTAL EVALUATION 40 3.4.1 Performance Comparison on Different Datasets 41 3.4.2 Performance Comparison under Different Parameters 45 3.4.3 Performance for Different Sorting Methods 47 3.4.4 Scalability of the Proposed Methods 47 3.4.5 Memory Usage of the Proposed Methods 49 3.4.6 Analysis of the Experimental Results 50 3.5 SUMMARY 51 CHAPTER 4 MINING MAXIMAL HIGH UTILITY ITEMSETS FROM DATA STREAMS WITH DIFFERENT MODELS 52 4.1 INTRODUCTION 52 4.2 PRELIMINARY AND DEFINITION 55 4.3 PROPOSED FRAMEWORK: GUIDE 58 4.3.1 GUIDELM: The Proposed Method for Landmark Model 61 4.3.2 GUIDESW: The Proposed Method for Sliding Window Model 64 4.3.3 GUIDETF: The Proposed Method for Time Fading Model 69 4.3.4 Generating MaxHUIs from MUI-Trees 71 4.3.5 The Pruning Strategy for MUI-Trees 73 4.4 EXPERIMENTAL EVALUATION 75 4.4.1 Performance of the Algorithms for Landmark Model 76 4.4.2 Performance of the Algorithms for Sliding Window Model 78 4.4.3 Effects of Bottom-Up Tracing Strategy 80 4.4.4 Quality of Patterns 83 4.5 SUMMARY 85 CHAPTER 5 MINING INTERESTING HIGH UTILITY USER BEHAVIOR PATTERNS IN MOBILE COMMERCE ENVIRONMENTS 86 5.1 INTRODUCTION 86 5.2 DEFINITION AND CONSTRAINT FORMULATION 90 5.3 PROPOSED METHOD: IM-SPAN 97 5.3.1 Mining High Utility Mobile Sequential Patterns 98 5.3.2 Mining Interesting High Utility Mobile Sequential Patterns 104 5.3 EXPERIMENTAL EVALUATION 111 5.4.1 Performance under Varied Thresholds 113 5.4.2 Performance under Varied Settings of Parameters 114 5.4.3 Analyses of Scalability and Memory Usage 116 5.4.4 Performance of Constraint Mining 118 5.4.5 Evaluation of Number of Patterns 119 5.4.6 Discussions about the Experimental Results 120 5.4 SUMMARY 121 CHAPTER 6 CONCLUSIONS AND FUTURE WORK 123 BIBLIOGRAPHY 126 VITA 133 PUBLICATIONS 134

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