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研究生:蘇聖富
研究生(外文):Sheng-fu Su
論文名稱:在串流資料中探勘多元關聯法則之研究
論文名稱(外文):A Study of Mining Multiple Association Rules from Data Streams
指導教授:李建億李建億引用關係
指導教授(外文):Chien-I Lee
學位類別:碩士
校院名稱:國立臺南大學
系所名稱:數位學習科技學系
學門:教育學門
學類:教育科技學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:31
中文關鍵詞:資料串流探勘頻繁樣式關聯法則
外文關鍵詞:data stream miningassociation rulefrequent pattern
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近年來,資料串流探勘已成為熱門的研究議題,並常被用來解決日益龐大的交易資料。然而,傳統的探勘方式,大多僅用單一模式(model)尋找交易頻繁的樣式,卻容易忽略了實際存在於交易活動中的交易行為模式。譬如,冰品在白天比熱食更為暢銷,將使熱食的交易行為不易被發現並容易被忽略。本研究使用動態串流樹(Dynamic Data Stream Tree)探勘多元關聯法則。使用動態多個循環模式找尋交易的行為樣式,遠比採用單一模式演算法更具多樣化、更準確,且更符合實際存在的交易模式。
Data stream mining is a hot research topic in recent years ,which can sovle the increasingly extensive transactions data. However, the traditional mining methods merely use the single model to find out these frequent trading patterns, which may ignore the practial trading activities in trading patterns easily. For instance, if the ices are sold much better than the hot foods in the daytime, the trading patterns of the hot foods may not be find out and it may be ignored esaily. The study uses dynamic data stream tree and mining multiple association rules. Using the multiple dynamic cycle models to look for the numerous transactions patterns would be more various, accurate and correspondent with the practical transaction patterns than the single model algorithm.
中文摘要i
ABSTRACTii
誌謝iii
目錄iv
表目錄vi
圖目錄vii
一、緒論1
1.1研究背景1
1.2研究動機及目的2
1.3論文架構與內容3
二、文獻探討4
2.1資料串流探勘4
2.2視窗模式4
2.2.1標界模式4
2.2.2傾斜式視窗模式4
2.2.3滑動視窗模式4
2.3相關演算法5
2.3.1精確演算法5
2.3.2近似演算法6
2.4關聯法則6
2.4.1Apriori演算法 7
2.4.2Fp-tree演算法 9
三、動態串流樹探勘演算法12
3.1介紹12
3.2DDST演算法12
3.2.1建立樹狀結構12
3.2.2新生模型樹17
3.2.3合併模型樹20
四、實驗結果與效能分析23
4.1實驗環境與評估方式23
4.2實驗資料集23
4.3效能評估24
五、結論與未來研究方向28
中文部份
陳柏宏(2007)。針對時空性交易資料提供動態視窗關聯法則探勘之研究,國立台南大學數位學習科技研究所碩士論文,未出版。
英文部分
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[13]C. Jin, W. Qian, C. Sha, J. X. Yu, and A. Zhou, "Dynamically maintaining frequent items over a data stream," Proceedings of the twelfth international conference on Information and knowledge management, pp. 287-294, 2003.
[14]W. Jinlong, X. Congfu, C. Weidong, and P. Yunhe, "Survey of the study on frequent pattern mining in data streams," Systems, Man and Cybernetics, 2004 IEEE International Conference on, vol. 6, 2004.
[15]R. M. Karp, S. Shenker, and C. H. Papadimitriou, "A simple algorithm for finding frequent elements in streams and bags," ACM Transactions on Database Systems (TODS), vol. 28, pp. 51-55, 2003.
[16]J. L. Koh and S. N. Shin, "An Approximate Approach for Mining Recently Frequent Itemsets from Data Streams," presented at DaWaK, 2006.
[17]C. K. S. Leung and Q. I. Khan, "DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams," Proceedings of the Sixth International Conference on Data Mining, pp. 928-932, 2006.
[18]H. F. Li, C. C. Ho, F. F. Kuo, and S. Y. Lee, "A New Algorithm for Maintaining Closed Frequent Itemsets in Data Streams by Incremental Updates," Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on, pp. 672-676, 2006.
[19]H. F. Li, S. Y. Lee, and M. K. Shan, "An efficient algorithm for mining frequent itemsets over the entire history of data streams," Proc. of the 1st Intl. Workshop on Knowledge Discovery in Data Streams, 2004.
[20]G. S. Manku and R. Motwani, "Approximate frequency counts over data streams," Proc. VLDB, vol. 2, pp. 346–357, 2002.
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