臺灣博碩士論文加值系統

資料探勘是一用以擷取隱藏在大量資料中，未知與潛在，但具有實用性資訊的資料分析方法。資料探勘的模式及方法，經過近幾年來相關研究的不斷改進，已見長足之發展，其中以關聯式規則的探勘方式最常被應用。關聯式規則的研究，著重於探討在大量資料中如何有效率、快速地找出單一階層的規則，近年來則有越來越多的學者開始研究多階層關聯式規則的問題，以便於在知識經濟的現代化社會裡，因應企業必須靈活運用更深入、更細緻的關聯式規則來協助管理者在短期間內完成決策的需求。為此，本研究提出一個有效率的資料結構FCET，以便於加速探勘廣義關聯式規則。
另外，隨著企業全球化的腳步加速，許多敏感性個人資料的蒐集、處理和應用，涉及到個人隱私的保護法令；此外，企業所管理的資料庫亦大幅成長，且由於資料庫中儲存著許多個人敏感性資料及公司機密資訊，一旦遭受不當存取，不僅造成資料庫安全問題，更有公司機密資料外洩、個人資料外流等嚴重問題，稍有不慎將導致企業競爭力因此受損。
本研究除了提出一個有效的的資料結構外，另外在探戡過程中亦導入隱私防護考量，建立一個從資料探勘及隱私保護兩個互相連動、息息相關的問題觀點進行完整性的探討，提出考慮以成本方式的貪婪演算法，搭配淨除及暴露兩種方法之保護機制，不僅達到確保公開內容的隱私保護，亦可確保從淨除後的資料庫中擷取出有用的資訊，進而有效達成隱私防護與知識擷取間的平衡。

Data mining is an analysis method used to extract the unknown and latent information that hides in large dataset which has usable information. In the last few years the data mining model and method have long-term progress and the association rule mining is most often applied. The association rule research focus on discussion how to discover single level association rule effectiveness in the large dataset. In the recent years more and more researchers start to study the problem of multiple level association rules that was advantageous in the knowledge economy modernized society. In accordance to the enterprise, it must utilize nimbly the more deeply and more detailed association rules to assist the superintendent to complete policy-making in the short time. For reach the above objective, this study proposed an efficient data structure, Frequent Closed Enumerable Table (FCET), to speed the generalized association rules mining.
In the other aspect, as a result of enterprise globalization acceleration, many sensitive individual information collection, processing and application involve to the individual privacy protection law. In addition, databases managed by enterprises also largely grow up. The databases store many individual sensitive material and corporation secret information. If the database suffers non-suitable access, it leads the security problem. Moreover, it causes the company secret restricted data and the individual material to be disclosed. Once the problem is not careful processed, it would possibly reduce the competitiveness of enterprise.
This study proposes an effective data structure which considers the privacy preserving in the mining process. In addition, it carries on the complete discussion from data mining and privacy the preserving related question. A greedy algorithm which considers the hiding cost was proposed here. The algorithm includes the sanitized procedure and exposed procedure protection of mechanism. Not only privacy preserving for public content but also useful information extraction are guarantee to reach. Moreover, after the sanitized processing, it achieves privacy preserving and knowledge extracting balanced effectively.

CHAPTER 1 Introduction
1.1 Background
1.2 Motivation
1.3 Objective
1.4 Research Method
1.5 Contributions
1.6 Organization
CHAPTER 2 Generalized Association Rule Mining Using an Efficient Data Structure
2.1 Problem Descriptions
2.2 Processing Flow of Mining Algorithms
2.2.1 Frequent Closed Enumeration Table(FCET)
2.2.2 FCET Index Tree
2.2.3 FCET Partition Tree for a Long Pattern
2.2.4 Infrequent Closed Enumeration Table(IFCET)
2.2.5 Calculating the Supports of non-leaf Items
2.3 Performance Evaluations
2.3.1 Simulation Model
2.3.2 Experimental Results
CHAPTER 3 Privacy Preserving Association Rule Mining Problem
3.1 Problem Descriptions
3.2 The Frame for Privacy Preservation
3.3 The Greedy Approach
3.3.1 Sanitizing Procedure
3.3.2 Exposed Procedure
3.3.3 Greedy Approximation Approach
3.3.4 Greedy Exhausted Approach
3.3.5 Evaluation Parameters
3.4 Performance Evaluation
3.4.1 Simulation Model
3.4.2 Experimental Results
CHAPTER 4 Privacy Preserving Association Rules by Using a Branch-and-Bound Algorithm
4.1 Problem description
4.2 The Processing Flow of a Branch-and-Bound Algorithm
4.3 The cluster criterions
4.4 Performance Evaluations
4.4.1 Simulation Model
4.4.2 Experimental Results
CHAPTER 5 Conclusions
References
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