無候選型樣產生之頻繁樹狀結構探勘｜國立中央大學博碩士論文系統

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研究生：	童俊宏 Jiun-Hung Tung
論文名稱：	無候選型樣產生之頻繁樹狀結構探勘 MINT: Mining Frequent Rooted Induced Unordered Tree without Candidate Generation
指導教授：	張嘉惠 Chia-Hui Chang
口試委員:
學位類別：	碩士 Master
系所名稱：	資訊電機學院 - 資訊工程學系 Department of Computer Science & Information Engineering
畢業學年度：	94
語文別：	中文
論文頁數：	36
中文關鍵詞：	子樹、標準型式、支持度、頻繁、型樣
外文關鍵詞：	canonical form, subtree, pattern, frequent, support
相關次數：	點閱：9 下載：0
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在資料探勘（Data Mining）的領域中樹狀結構的探勘（Tree Mining）是一個重要的問題，它可以應用在網站記錄（Web Logs）的分析、生物資訊（Bioinformatics）和半結構式的文件（Semi-structured Documents）上。然而在此方面的先前研究都是先產生候選型樣，再測試其是否為頻繁出現的型樣，如果不是則會被刪除。以這樣的做法會用都掉很多的時間及空間在候選者的產生與測試上。所以，在此篇論文裡面，我們使用區域頻繁的這個概念設計了一個不會有候選者產生的演算法來做「有樹根的」、「誘導的」、「無序的」樹狀結構的探勘工作，而我們把這個演算法稱為MINT。我們利用資料產生器產生一些人工合成的資料集，以及實際的網站記錄資料，和HybridTreeMiner 來做比較。實驗結果顯示出即使在樹狀結構這種複雜的資料型態中，使用找尋區域頻繁的觀念是依然可以有不錯的效能。

Tree pattern mining is an important issue in data mining area and it has many emerging applications including web log analysis, bioinformatics, semi-structured documents, and so on. However, most of the previous works are candidate-generation-and-testing approach. They enumerate candidate patterns from shorter patterns based on the apriori frequent patterns. Because this approach costs a lot of time and space in candidate generation and testing, in this paper, we adopt the idea of pattern growth to mine frequent rooted induced unordered tree without candidate generation. In the performance study, we use synthetic datasets and real world application datasets to compare with HybridTreeMiner. The experiments show that our algorithm is an efficient algorithm and cost-effective.

目錄..........................................................................I
圖目錄......................................................................III
表目錄...................................................................... IV
第一章 緒論...................................................................1
1.1. 研究動機與目的...........................................................1
1.2. 論文架構.................................................................3
第二章 問題定義...............................................................4
第三章 相關研究...............................................................8
3.1. Unot 演算法..............................................................9
3.2. uFreqt 演算法...........................................................10
3.3. HybridTreeMiner 演算法..................................................10
3.4. RootedTreeMiner 演算法..................................................12
3.5. 相關研究之比較..........................................................12
第四章 演算法................................................................14
4.1. 樹狀結構探勘的挑戰......................................................14
4.2. 演算法架構..............................................................15
4.2.1. 標準型式（Canonical From） ...........................................15
4.2.2. 型樣列舉方法（Enumeration）...........................................17
4.2.3. 延伸點所允許的標記值範圍計算(Label Range Computing)...................19
4.2.4. 樹型樣成長機制（Extension）...........................................21
4.3. 演算法..................................................................25
第五章 實驗結果..............................................................27
5.1 合成資料集...............................................................27
5.1.1 資料產生器說明.........................................................27
5.1.2 合成資料集實驗分析.....................................................29
5.2 實際資料集...............................................................30
5.2.1 實際資料集實驗分析.....................................................30
第六章 結論..................................................................33
參考文獻.....................................................................34

                                

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