Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/19746
DC FieldValueLanguage
dc.contributor詹進科zh_TW
dc.contributorChan-Chin Keen_US
dc.contributor高勝助zh_TW
dc.contributor何健明zh_TW
dc.contributor陳孟彰zh_TW
dc.contributorGau-Sheng Juen_US
dc.contributorJian-Ming Hoen_US
dc.contributorMeng-Jang Chenen_US
dc.contributor.advisor廖宜恩zh_TW
dc.contributor.advisorI-En Liaoen_US
dc.contributor.author林克仲zh_TW
dc.contributor.authorLin, Ke-Chungen_US
dc.contributor.other中興大學zh_TW
dc.date2012zh_TW
dc.date.accessioned2014-06-06T07:07:30Z-
dc.date.available2014-06-06T07:07:30Z-
dc.identifierU0005-0902201109083300zh_TW
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dc.identifier.urihttp://hdl.handle.net/11455/19746-
dc.description.abstract在此篇論文中我們提出兩個重要的方式去解決混合式高頻序列探勘及高頻項目集探勘的問題。混合式高頻序列及高頻項目集這兩種技術的重要性在於可以提供市場分析裡所需要的重要資訊。而當所需要處理的資料及複雜度增加時,高效率的計算則成為了這兩種技術不可或缺的考量,故在此論文中,我們首先提出一個階層式的探勘技術來解決效率上的問題。在這個階層式探勘技術中主要包含兩種技術,第一是利用計數一個序列來取代計數一個群組中所有序列的概念,第二是利用交易分解的方式來計數支持度。而後,我們又提出另一種改良交易分解的演算法來探勘高頻項目集,利用分群的方式將資料庫內的所有項目集分成不同的群組,再在這些群組內執行交易分解的方法來探勘出所有的高頻項目集。從實驗數據顯示,我們提出的方法在執行效率上都有很優良的表現。 我們在此論文中一共有四個貢獻,第一,我們提出利用一個序列來取代一整個群組中的所有序列,當此一序列被計數時,同時也計數到了群組中的所有序列;第二,我們提出了一種交易分解的探勘方式,利用此種探勘方式,我們可以降低I/O的成本輸出;第三,我們在論文中証明了交易分解的正確性;最後,我們提出了改良交易分解的另一演算法,並實作其他演算法來和我們所提出的演算法進行效率上的比較。zh_TW
dc.description.abstractIn this thesis, we propose two important methods to deal with the problems of mining hybrid sequential patterns and mining frequent itemsets. Hybrid sequential patterns and frequent itemsets are two mining techniques which provide useful information for the improvement of the analysis of marketing strategies. High-performance computing is crucial for these two techniques in order to ensure system scalability when the size and complexity of the database increase. We first propose a hierarchical mining technique to deal with the performance issue. The unique features of this new technique include the following: counting hybrid sequential patterns by class and examining database transactions in a top-down manner. Further, in order to improve the space problem in the transaction decomposition method, we propose another algorithm that combines the transaction decomposition method and the clustering technique. The improved algorithm separates the lattice into small pieces so that each portion can be solved by the decomposition method independently. Experimental results show that this improved algorithm has a fast execution time with respect to the mining of a transaction database. In summary, this thesis makes four major contributions to the field of data mining. First, a new method to count a group of patterns simultaneously is provided by the proposed pattern-class concept. Second, a novel decomposition model to lower the I/O cost for counting the patterns from a large database is proposed. Third, the correctness of counting the patterns in the decomposition method is proved. Lastly, an algorithm for improving the decomposition method is introduced, and the performance comparison of the proposed algorithm with the existing algorithms is presented in this thesis.en_US
dc.description.tableofcontentsContents Chapter 1 Introduction …………………………………………………………….. 1 1.1 Thesis Motivations …………………………………………………………..1 1.2 Major Contributions .………………………………………………………. 3 1.2.1 Hybrid Sequential Patterns Mining …………………………………. 4 1.2.2 Association Rules Mining ……………………………………………4 1.3 Thesis Organization…………………………………………………….……5 Chapter 2 Bottom-Up Approach and Top-Down Approach …………....……...6 2.1 Bottom-UP Approach ……...…….………………………………………....6 2.1 Top-Down Approach ……...…….………………………………………….8 Chapter 3 Mining Hybrid Sequential Patterns …………………………………..10 3.1 Introduction ……………………………..………………………………….10 3.2 Related Work .…………..………………………………………………….14 3.2.1 Sequential Patterns …….…………………………………………....14 3.2.1.1 The AprioriAll Algorithm …………………………………...14 3.2.1.2 The GSP Algorithm ………………………………………….15 3.2.1.3 The SPADE Algorithm ………………………………………16 3.2.2 Hybrid Sequential Patterns …….…………………………………....17 3.2.3 The GFP2 Algorithm ……….….…………………………………....19 3.3 The Hierarchical Mining Technique ………………………………………..19 3.3.1 Characteristic Pattern and Pattern Class …………………………….20 3.3.2 Counting Hybrid Patterns by Class Counting ………………………22 3.3.3 A Decomposition Model for Characteristic Patterns ………………..26 3.3.4 An Example of Counting Characteristic Patterns …………………...30 3.4 The Top-Down Mining Algorithm …………………………………………33 3.4.1 Counting Class Members and The Pattern-Table …………………...33 3.4.2 TDM Algorithm ……………………………………………………..38 3.4.3 Complexity Analysis ……………….……………………………...42 3.5 Experimental Results ……………………………………………………….43 3.6 Conclusions ………………………………………………………………...49 Chapter 4 Mining Frequent Itemsets ……………………………………………..51 4.1 Introduction ………………………………………………………………...51 4.2 Related Work ……………………………………………………………….53 4.2.1 The Apriori Algorithm ………………………………………………53 4.2.2 The DHP Algorithm ………………………………………………...55 4.2.3 The Pincer-Search Algorithm ……………………………………….55 4.2.4 The FP-Growth Algorithm ………………………………………….55 4.2.5 Mining Algorithm with Cluster Technique ………………………….56 4.3 Problem Description ………………………………………………………..56 4.4 TDC Method (Transaction Decomposition with Clustering) ………………60 4.4.1 Itemset Cluster Technique …………………………………………..61 4.4.2 The TDC Algorithm ……….………………………………………..64 4.4.3 Complexity Analysis ………………………………………………..66 4.5 Experimental Results ………………………………………………………66 4.6 Conclusions ……………………………………………………………...…71 Chapter 5 Conclusions and Future Work ……………………………………...72 5.1 Conclusions ……………………………………………………………...…72 5.1.1 Hybrid Sequential Patterns ……..…………………………………...73 5.1.2 Frequent Itemsets Mining ……..……………….…………………....73 5.2 Future Work …………………………………………………………….…74 References ………………………………………………………………………….76zh_TW
dc.language.isoen_USzh_TW
dc.publisher資訊科學與工程學系zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-0902201109083300en_US
dc.subjectdata miningen_US
dc.subject資料探勘zh_TW
dc.subjectdecomposition methoden_US
dc.subjectfrequent itemseten_US
dc.subject交易分解zh_TW
dc.subject高頻項目集zh_TW
dc.title一個有效的由上而下探勘頻繁項目的方法zh_TW
dc.titleAn Efficient Top-Down Approach for Mining Frequent Patternsen_US
dc.typeThesis and Dissertationzh_TW
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