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研究生:徐勝榮
研究生(外文):Shen-jung Hsu
論文名稱:在大型動態交易資料庫中探勘循序樣式之研究
論文名稱(外文):A Study of Mining Sequential Pattern in Large Dynamic Transaction Database
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:資訊教育研究所碩士班
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:2004
畢業學年度:92
語文別:中文
論文頁數:49
中文關鍵詞:資料探勘循序樣式動態交易資料庫頻繁序列
外文關鍵詞:Data MiningDynamic Transaction DatabaseFrenquent SequenceSequential Pattern
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探勘循序樣式(sequential patterns)是資料探勘中一個重要的研究議題,但目前大部分探勘循序樣式的研究工作仍假設交易資料庫是靜態的。然而,在實際的應用裡,交易資料庫確是會隨著時間因顧客交易資料的加入或刪除而不斷改變。近年來連鎖加盟企業的興起,亦使得交易資料庫合併探勘的工作顯得更為重要。因此,在交易資料庫變動之後,如何維護最新且有效的循序樣式成為了一件重要的工作。本論文,在交易資料庫的遞增探勘議題中,提出Incremental Sequence Pattern mining (ISP)演算法,利用原始資料庫中發掘的頻繁序列,和存在於遞增資料庫中的項目才會影響候選序列支持度的概念,來減少產生的候選序列數。再則,於交易資料庫同時新增刪除的探勘議題中,解決原始資料庫被刪除,造成之前探勘所獲得的資訊無法再使用的情形,提出Updated Sequence Pattern mining(USP)演算法。最後,對於交易資料庫合併探勘的議題,利用之前在每個資料庫中探勘所獲得的資訊,提出Merged Sequence Pattern mining(MSP)演算法,來減少合併探勘所產生的候選序列數。三種演算法的主要概念,皆是有效的利用之前探勘所獲得的資訊,來減少探勘過程中所產生的候選序列數,以降低重新探勘整個交易資料庫的成本。由實驗結果得知ISP法在遞增資料庫中的頻繁項目愈少,或者與原始資料庫中的頻繁項目間的差異性愈大時,效果越佳。而USP法對於資料庫異動後新產生的頻繁項目愈少時,或者與原始資料庫中舊的頻繁項目間的差異性愈大時,效果愈佳。而MSP法則是在兩個資料庫中相同的頻繁項目愈少時,效果愈佳。由實驗得知,此三種演算法在多種模擬資料上的執行時間快過GSP法且比GSP法使用較少的記憶體空間。
Mining sequential pattern plays an important role in data mining. Numerous algorithms have been proposed to mining sequential patterns efficiently in a static database. However, the maintenance of such discovered sequential patterns is nontrivial in large database. In real world, a transaction database may allow the users to insert/delete the transaction data and the customer data frequently. Additionally, the development of multiple shop leads in the urgent requirement of fast mining sequential patterns in the merged database. Therefore, the efficient maintenance of the latest sequential patterns in dynamic large database becomes more and more important. First, this study presents the incremental sequential patterns mining (ISP) algorithm to maintain the discovered sequential patterns in incremental transaction database efficiently. ISP applies the information of the discovered sequential patterns in original database to reduce the number of candidates. Consequently, the performance of ISP is better than that of GSP in our experimental results. Furthermore, this study also proposes the updated sequential patterns mining (USP) algorithm, which extends the concept of reducing the number of candidates to support both insertion and deletion operations in an update database efficiently. For the issue of efficient mining sequential patterns in the merged database, this study develops the merged sequential patterns mining (MSP) algorithm. Contract to re-mine a merged large database, MSP uses two small database, which have discovered their sequential pattern, respectively to speed up the mining process. Simulation results reveal that ISP, USP and MSP are superior to GSP, respectively in several artificial datasets. The more difference of the frequent sequential pattern between original database and incremental database, the more performance difference between ISP and GSP. USP is very efficient when the proportion of the updated part of the database is small. The performance difference is greater between MSP and GSP when the proportion of the common frequent 1-items in two unmerged database is smaller. Simulation results also appear that the three algorithms use less memory than that of GSP, respectively.
中文摘要 ……………………………………………………………… Ⅰ
英文摘要 ……………………………………………………………… Ⅱ
誌謝 ……………………………………………………………… Ⅲ
目 次 ……………………………………………………………… Ⅳ
表 次 ……………………………………………………………… Ⅳ
圖 次 ……………………………………………………………… Ⅵ
第一章 緒論………………………………………………………… 1
第一節 研究背景…………………………………………………… 1
第二節 研究動機…………………………………………………… 2
第三節 論文架構與內容…………………………………………… 3
第二章 文獻探討…………………………………………………… 4
第一節 循序樣式之探勘…………………………………………… 4
第二節 循序樣式的遞增探勘……………………………………… 5
第三章 遞增探勘演算法…………………………………………… 7
第四章 同時遞增遞減之探勘演算法……………………………… 13
第五章 資料庫合併之探勘演算法………………………………… 20
第六章 實驗結果…………………………………………………… 28
第一節 模擬資料產生……………………………………………… 28
第二節 實驗結果…………………………………………………… 30
第七章 結論與未來研究方向……………………………………… 46
第一節 結論………………………………………………………… 46
第二節 未來研究方向……………………………………………… 46
參考文獻 ……………………………………………………………… 48
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[2] A. Ayad, N. El-Makky, and Y. Taha, “Incremental Mining of Constrained Association Rules,” In Proc. First SIAM International Conference on Data Mining, Chicago, IL, Apr. 2001.
[3] D. W. Cheung, J. Han, V. T. Ng, and C. Y. Wong, “Maintenance of Discovered Association Rules in Large Databases: An Incremental Update Technique,” In Proc. 12th International Conference on Data Engineering, New Orleans, LA, pp.106-114, Feb. 1996.
[4] D. W. Cheung, S. D. Lee, and B. Kao, “A General Incremental Technique for Maintaining Discovered Association Rules,” In Proc. 5th International Conference on Database Systems for Advanced Applications, Melbourne, Australia, pp. 185-194, Apr. 1997.
[5] F. Masseglia, P. Poncelet, and M. Teisseire, “Incremental Mining of Sequential Patterns in Large Databases,” Data & Knowledge Engineering, Vol. 45, No. 1, pp. 97-121, 2003.
[6] S. Parthasarathy, M. J. Zaki, M. Ogihara, and S. Dwarkadas, “Incremental and Interactive Sequence Mining,” In Proc. 8th International Conference on Information and Knowledge Management, Kansas City, MO, pp. 251-258, Nov. 1999.
[7] J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, and M. Hsu, “PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth,” In Proc. 17th IEEE International Conference on Data Engineering, Heidelberg, Germany, pp. 215-224, Apr. 2001.
[8] N. Sarda and N. V. Srinivas, “An Adaptive Algorithm for Incremental Mining of Association Rules,” In Proc. 9th International Workshop on Database and Expert Systems Applications, Vienna, Austria, pp. 240-245, Aug. 1998.
[9] R. Srikant and R. Agrawal, “Mining Sequential Patterns: Generalizations and Performance Improvements,” In Proc. 5th International Conference on Extending Database Technology, Avigonon, France, pp.3-17, Mar. 1996.
[10] S. Thomas, S. Bodagala, K. Alsabti, and S. Ranka, “An Efficient Algorithm for the Incremental Updating of Association Rules in Large Database,” In Proc. 3rd International Conference On Knowledge Discovery and Data Mining, Newport Beach, California, pp. 263-266, Aug. 1997.
[11] M. J. Zaki, “Efficient Enumeration of Frequent Sequences,” In Proc. 7th International Conference on Information and Knowledge Management, Bethesda, MD, pp. 68-75, Nov. 1998.
[12] M. Zhang, B. Kao, C. L. Yip, and D. Cheung, “A GSP-based Efficient Algorithm for Mining Frequent Sequences,” In Proc. 2001 International Conference on Artificial Intelligence, Las Vagas, Jun. 2001.
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