近年來，利潤探勘演算法被設計用來衡量量化資料庫裡商品的利潤，以用來找出高利潤的項目集。透過商品的利潤與價格等因素，更有效的知識將被探勘出來以提供給管理者。現今的方法，大多以批次的方式來找出高利潤項目集。在現實應用中，交易資料可能隨時地被新增、刪除、或修改。以批次的方式來處理，則必須重掃整個更新後的資料庫，以用來維護更新後的知識。在本篇論文的第一部分，基於準大法則(Pre-large concepts)，我們提出了有效的漸進式及遞減式的利潤探勘演算法，當交易資料被新增或移除時，我們設計的演算法能有效的更新並維護我們已探勘出的高利潤項目集。我們首先必須判斷每個項目集在原始資料庫中是否為大(高)加權交易利潤項目集，準大加權交易利潤項目或小(不存在於發現的知識中)加權交易利潤項目集，並將其分成三個部分與九種情況做討論，每個部份將根據我們所設計的演算法去進行資料的維護。基於準大法則的概念，我們只需要針對少量項目集進行資料庫重新掃描便可進行高利潤項目集的維護。
然而，在利潤探勘資料蒐集與數據傳播的過程中，可能引發隱私資料外洩風險。敏感資料或個人隱私在分享或是發佈時，應該加以隱匿受到保護。也因此，隱私保護之利潤探勘成為近年來重要的研究議題之一。在本篇論文第二部分，我們提出兩種透過新增及刪除資料的方法來隱藏敏感項目集。在這二個方法裡，我們提出一個演化式的隱私保護利潤探勘方法，透過遺傳演算法找出最合適的交易集做為新增或刪除之交易，進而隱藏住敏感項目集。此方法設計了三個變數來設計一個彈性的評估函數，並可根據使用者的喜好彈性地分配此三個變數的權重。透過在第一部分提出的準大加權交易利潤項目集的概念，我們可以減少遺傳演算法在評估染色體時重新掃描資料庫的時間成本，以加快評估的過程。最後，我們也透過實驗結果來評估演算法的效能。

Utility mining algorithms have recently been proposed to discover high utility itemsets from a quantitative database. Factors such as profits or prices are concerned in measuring the utility values of purchased items for revealing more useful knowledge to managers. Nearly all the existing algorithms are performed in a batch way to extract high utility itemsets. In real-world applications, transactions may, however, be inserted, deleted or modified in a database. The batch mining procedure requires more computational time for rescanning the whole updated database to maintain the up-to-date knowledge. In the first part of this thesis, two algorithms for data insertion and data deletion are respectively proposed for efficiently updating the discovered high utility itemsets based on pre-large concepts. The proposed algorithms firstly partition itemsets into three parts with nine cases according to whether they are large (high), pre-large or small transaction-weighted utilization in the original database. Each part is then performed by its own procedure to maintain and update the discovered high utility itemsets. Based on the pre-large concepts, the original database only need to be rescanned for much fewer itemsets in the maintenance process of high utility itemsets.
Besides, the risk of privacy threats usually exists in the process of data collection and data dissemination. Sensitive or personal information are required to be kept as private information before they are shared or published. Privacy-preserving utility mining (PPUM) has thus become an important issue in recent years. In the second part of this thesis, two evolutionary privacy-preserving utility mining algorithms to hide sensitive high utility itemsets in data sanitization for inserting dummy transactions and deleting transactions are respectively proposed. The two evolutionary privacy-preserving utility mining algorithms find appropriate transactions for insertion and deletion in the data-sanitization process. They adopt a flexible evaluation function with three factors. Different weights are assigned to the three factors depending on users’ preference. The maintenance algorithms proposed in the first part of this thesis are also used in the GA-based approach to reduce the cost of rescanning databases, thus speeding up the evaluation process of chromosomes. Experiments are conducted as well to evaluate the performance of the proposed algorithms.

論文審定書 i
致謝 ii
摘要 iii
Abstract iv
Contents v
List of Figures viii
List of Tables xi
CHAPTER 1 Introduction 1
1.1 Motivation 1
1.2 Contributions 4
1.3 Organization of Thesis 5
CHAPTER 2 Related Work 7
2.1 Association Rule Mining 7
2.2 The Concept of Pre-large Itemsets 10
2.3 High Utility Mining 14
2.4 Genetic Algorithms 15
2.5 Data Sanitization 18
CHAPTER 3 The Maintenance Algorithms for Mining High Utility Itemsets in Dynamic Database 20
3.1 Introduction 20
3.2 The Maintenance Algorithm for Transaction Insertion 20
3.2.1 Notation 22
3.2.2 Theoretical Foundation 24
3.2.3 The Proposed Incremental High Utility Mining Algorithm 26
3.2.4 An Illustrated Example 32
3.2.5 Experimental Results 42
3.3 The Maintenance Algorithm for Transaction Deletion 52
3.3.1 Notation 53
3.3.2 Theoretical Foundation 55
3.3.3 The Proposed Decremental High Utility Mining Algorithm 57
3.3.4 An Illustrated Example 62
3.3.5 Experimental Results 71
CHAPTER 4 The GA-based Algorithms for Privacy Preserving Utility Mining 80
4.1 Introduction 80
4.2 The Representation of Chromosomes 80
4.3 Fitness Function 82
4.4 The Pre-large Concepts and Proposed Sliding Count 87
4.5 Genetic Operators 89
4.5.1 Crossover 89
4.5.2 Mutation 89
4.5.3 Selection 90
4.6 A GA-based Approach for Privacy Preserving Utility Mining through Transaction Insertion 90
4.6.1 Notation 91
4.6.2 The Flowchart of Proposed Algorithm 92
4.6.3 The Proposed PPUM Approach through Transactions Insertion 93
4.6.4 An Illustrated Example 97
4.6.5 Experimental Results 106
4.7 A GA-based Approach for Privacy Preserving in Utility Mining through Transaction Deletion 110
4.7.1 Notation 111
4.7.2 The Flowchart of Proposed Algorithm 112
4.7.3 The Proposed GA-based Approach through Transaction Deletion 113
4.7.4 An Illustrated Example 117
4.7.5 Experimental Results 123
CHAPTER 5 Conclusion and Future Work 129
References 131

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