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研究生:吳珮芸
研究生(外文):Pei-Yun Wu
論文名稱:以有效產生候選項目集合及計算支持度技術發掘關聯規則之研究
論文名稱(外文):An Efficient Generation of Candidate Itemsets and Count Algorithm for Mining Association Rules
指導教授:廖斌毅潘正祥
指導教授(外文):Bin-Yih LiaoJeng-Shyang Pan
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:電子與資訊工程研究所碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:77
中文關鍵詞:關聯規則快速產生候選項目集合結合樹
外文關鍵詞:Association ruleEfficient Generation of Candidate Itemsetsjoint set tree
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EGC演算法最主要是利用在結合高頻項目集合前先判斷其是否具有足夠條件結合為一個重要的候選項目集合,以過濾不重要之候選項目集合。除此之外,由於雜湊樹的深度是取決於項目集合的長度,而往往項目集合愈小的時候,其項目集合的數量都很龐大。因此當項目集合長度較小時,雜湊樹會花費相當多的時間將大量的候選項目集合分割成少數部份。而EGC演算法利用了簡單的集合樹儲存候選項目集合及計算其支持度,以縮減其執行時間。另外,EGC演算法在每一階段都利用DCP其中一個使用高頻項目集合來縮減資料庫的方法來有效地刪減資料庫,而非採用DHP演算法。由實驗結果,可以很清楚地看出,EGC演算法可利用比Apriori, DHP, IHPwoTTP及IHPwTTP更少的執行時間便找到正確的關聯規則。另外,我們利用EGC在真實的投資者資料庫中找出喜愛投資各類股的投資者類型,藉由找到的關聯規則可提供證券商的營業員推薦給投資者較易接受的投資組合之參考。
Mining association rules from transaction databases is one of important techniques in data mining. Applications of association rules extend to discovering frequent patterns in consumer behavior, marketing analysis, electronic commerce and education, and other areas. In this thesis, we developed EGC, is an efficient algorithm for mining association rules. The main improvements are EGC uses an innovative method for generating candidate itemsets by checking the numbers of the preceding frequent itemsets before joining procedure. And EGC uses the simple tree data structure for storing the candidate itemsets and counting their supports. In addition, EGC uses the database global pruning method of DCP for efficiently reducing the size of the database. The experiments show that the performance of EGC is better than Apriori and DHP,IHPwoTTP, and IHPwTTP. The execution time and memory required of EGC are less than Apriori, DHP, IHPwoTTP, and IHPwTTP. In other applications, EGC can efficiently mine interesting information from investor databases to provide the optimal portfolio for each investor of the brokerage securities firm.
Chapter 1. Introduction --------------------------------------1
Chapter 2. Related work --------------------------------------4
2.1. Apriori algorithm ---------------------------------------6
2.2.DHP algorithm --------------------------------------------8
2.3.Sampling algorithm --------------------------------------11
2.4.DIC algorithm -------------------------------------------12
2.5.HD algorithm --------------------------------------------13
2.6.IHP algorithm -------------------------------------------14
2.6.1.IHPwoTTP approach--------------------------------------14
2.6.2.IHPwTTP approach---------------------------------------18
Chapter 3. EGC algorithm ------------------------------------19
3.1.Data structure for EGC algorithm ------------------------20
3.2.Pruning method for EGC algorithm ------------------------22
3.3.Database reduction method for EGC algorithm--------------26
3.4.Procedures of EGC algorithm------------------------------28
3.5.The comparison among the EGC and other algorithms--------35
3.5.1.Candidate data structure-------------------------------35
3.5.2.Pruning method-----------------------------------------36
3.5.3.Database reduction method------------------------------38
Chapter 4. Performance and Application analysis -------------39
4.1.Performance analysis in IBM synthetic databases----------39
4.2.Application analysis in the real investor database ------49
Chapter 5. Conclusions --------------------------------------50
References --------------------------------------------------52
Appendix 1.The survey of public opinions of investors -------54
Appendix 2.The symbol of various items of the survey --------56
Appendix 3.The rules from the investor database--------------58
Appendix 4.The survey of public opinions of investors-Chinese version------------------------------------------------------62
Autobiography------------------------------------------------64
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