高效益型樣探勘(High Utility Pattern Mining)是資料探勘領域中一門新興的研究議題，其與傳統型樣探勘問題不同之處乃在於其不只考慮資料集中項目之發生頻率或相互關聯等特性，同時還加入了項目的效益屬性(例如:價值、重要度等)此新概念，因此能廣泛運用於商業、生醫、工程等領域中。高效益項目集探勘(High Utility Itemset Mining)為其中一個重要的研究議題，其主要目的是從程序資料庫中挖掘出效益值不低於使用者自訂之最小效益門檻值(Minimum Utility Threshold)的項目集(例如:利潤高於門檻值的商品組合)。雖然近年已有許多相關研究被提出，但現今研究仍存有以下不足之處: (1)使用者有不易設定最小效益門檻值之問題。若門檻值設定過高，將產生太少型樣；反之，若門檻值設定過低，將產生太多不重要的型樣並降低執行效率。對於想找出最重要之高效益項目集的使用者而言，缺乏有效率之方式。(2)對於需用門檻值探勘高效益項目集的使用者而言，現今方法往往在探勘過程中產生過多冗餘的項目集，因而導致探勘工作之執行效能及儲存空間等問題，而此問題在處理密集型資料集時變得更加嚴重。(3)雖然事件資料庫(Event Database)這種循序資料在許多應用中很常見，然而，目前尚未存有能針對這種較複雜的資料有效率挖掘高效益型樣的方法。
為了解決上述議題，本論文探討了一系列新穎的高效益型樣探勘問題：(1)前k高效益項目集探勘(Top-k High Utility Itemset Mining)；(2)封閉高效益項目集探勘(Closed+ High Utility Itemset Mining)；(3)高效益情節探勘(High Utility Episode Mining)。
在第一個研究主題中，本論文針對使用者不易設定門檻值之問題，率先提出一個創新探勘前k高效益項目集的架構，其中k 代表的是使用者期望找到的高效益項目集個數。此外，我們還提出第一個有效率從交易資料庫挖掘前k高效益項目集的演算法TKU (mining Top-K Utility itemsets)及其相關技術。實驗結果顯示TKU能夠非常有效率地從交易資料庫中挖掘前k高效益項目集。
在第二個研究主題中，本論文率先提出一個不失真的高效益項目集精簡表示法，稱為封閉高效益項目集(Closed+ High Utility Itemset)，簡稱CHUI，以解決現今方法在探勘過程中產生過多冗餘項目集之問題。只挖掘CHUI不僅能減少探勘所需的時間及記憶體使用量，也不會遺失任何高效益項目集的資訊。因此CHUI又稱為不冗餘的高效益項目集。為了挖掘CHUI，我們提出三個有效率的演算法AprioriHC、AprioriHC-D及CHUD (Closed+ High Utility itemset Discovery)。不僅如此，我們還提出第一個有效率從CHUI還原所有高效益項目集的方法DAHU (Derive All High Utility itemsets)。實驗結果顯示，所提出的精簡表示法能夠非常有效地壓縮高效益項目集，且所提出的方法能夠非常有效率地挖掘CHUI。合併CHUD與DAHU不僅能找出所有高效益項目集，其執行效能甚至優於既存最優之高效益項目集探勘方法。
在第三個研究主題中，本研究針對事件資料庫這種複雜度更高的循序資料，率先提出一個新穎探勘高效益情節的架構，用來找出高效益且相關的循序事件組合，也就是高效益情節。在此架構下，我們提出一套計算情節效益值的方式，並提出第一個探勘高效益情節的演算法UP-Span (Utility ePisodes mining by Spanning prefixes)。實驗結果顯示，UP-Span 能夠非常有效率地挖掘高效益情節且具備優良的延展性。
本研究中所探討的三個主題之共同目的，乃基於實際應用上之需求及現今方法不足之處，提出一系列新穎之高效益型樣探勘演算法，有效性地提供使用者精簡且重要的高效益型樣。我們將上述三個研究主題有系統地整合於本論文中，從理論及實務的角度分析所提方法之優點及限制，並經由一系列之實驗驗證所提出新創方法之優越性。本研究之主要貢獻一方面對高效益型樣探勘領域之學理研究演進可產生高度之影響性，另一方面則將能廣泛運用於各種領域之應用中，對人類生活產生創新之價值。

High utility pattern mining is an emerging topic in data mining, which differs from traditional pattern mining in that it considers not only the basic properties of items (like frequency or associations) but also the utility (e.g., profit) as a new concept. Hence, it can be applied to various kinds of domains like commerce, biomedicine, and engineering. One important research issue in this field is high utility itemset (abbreviated as HUI) mining, which refers to discovering itemsets having a utility no less than a user-specified minimum utility threshold min_util in transactional databases. Though many studies have been conducted on HUI mining, existing methods incur the following deficiencies: First, it is not easy for users to set an appropriate min_util threshold. Inappropriate setting of this threshold will result in few or too large HUIs. Second, for the users who need to use min_util to find HUIs in dense databases, existing methods may produce too many redundant HUIs, which not only seriously degrades the performance of the mining task but also makes it difficult for analysis. Third, HUI mining cannot discover ordered sets of items carrying high utility in sequential data such as event database even though event databases are essential to many real-life applications.
To resolve the above issues and meet the different requirements in various applications, this dissertation addresses a series of novel high utility pattern mining problems, including (1) top-k high utility itemset mining, (2) closed+ high utility itemset mining and (3) high utility episode mining.
Regarding the first research topic, we address the first problem mentioned above by proposing a novel framework called top-k high utility itemset mining, where k is the desired number of HUIs to be mined. In this framework, we propose an efficient algorithm called TKU (mining Top-K Utility itemsets) for efficiently mining the complete set of top-k high utility itemsets in transactional databases. Experiments show that the performance of TKU is very close to that of the optimal case of state-of-the-art HUI mining algorithms.
Regarding the second research topic, we tackle the problem of redundancy of HUIs by proposing a lossless and compact representation of HUIs, named closed+ high utility itemset (abbreviated as CHUI). Mining CHUI reduces both the execution time and the memory consumption of the mining task. CHUIs are non-redundant HUIs because the set of all the CHUIs retains information of all the HUIs. Three algorithms respectively called AprioriHC AprioriHC-D and CHUD (Closed+ High Utility itemset Discovery) are proposed for efficiently mining CHUIs. Moreover, a novel algorithm called DAHU (Derive All High Utility itemsets) is proposed for efficiently recovering all HUIs from CHUIs. Experiments show that the proposed algorithms are very efficient even in some cases where the state-of-the-art algorithms fail to complete the mining task.
As to the third research topic, we propose the first framework for mining high utility episodes (abbreviated as HUEs), that is, ordered sets of relevant events carrying high utility. In this framework, we define a new way to calculate the utility of episodes and propose an efficient algorithm called UP-Span (Utility ePisodes mining by Spanning prefixes) for mining the complete set of HUEs in event databases.
The above three research topics are connected around a major research aim: Devising a series of novel utility mining methods for efficiently discovering different types of high utility patterns from transactional or event databases, with the goal of providing users important and concise mining results. Experimental results on both real and synthetic datasets show that the proposed algorithms outperform the state-of-the-art utility mining algorithms significantly. As an ultimate goal, this dissertation contributes in achieving impactful research methods and high-potential applications for human life with advancing the emerging field of utility pattern mining.

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