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研究生:梁伊麟
研究生(外文):LIANG YI LIN
論文名稱:布林表示法為基底的關聯式挖掘方法
論文名稱(外文):A Boolean Expression-Based Approach for
指導教授:陳省隆
指導教授(外文):Hsing-Lung Chen
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:電子工程系
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:2001
畢業學年度:89
語文別:中文
論文頁數:51
中文關鍵詞:資料挖掘關聯式規則高頻項目集布林基底表示屬性分群
外文關鍵詞:data miningassociation rulesfrequent itemsetsBoolean-based expressionAttribute grouping
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  • 被引用被引用:0
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近年來,資料挖掘技術的研究受到重視。而在電子商務應用上最常利用挖掘關聯式規則法,由於關聯式規則法是找出交易記錄中有相關的項目組成高頻項目集並以多個屬性推出一個屬性結果的規則,找出交易記錄中有相關的項目組成高頻項目集。而搜尋高頻項目集的方法,是影響整個挖掘過程效能的關鍵要素。而以往的關聯式規則挖掘方法,由於必須重覆掃瞄資料庫,使得規則產生的等待時間增長,而且結合成高頻項目集時必須儲存所有候選項目集,如此會使用到大量的運算和儲存記憶空間,造成效率不佳。
本論文提出一種布林基底表示的關聯式規則分群方法。經由布林運算的方式,找出資料庫中屬性的關聯性,達到屬性分群,以減少產生高頻項目集時,儲存候選項目時所需的記憶體空間和大量的運算。最後經由模擬,比較其他關聯式挖掘演算法,整體而言,我們的布林基底表示的分群演算法比其他演算法,有較好的效率。

The data mining technique is getting more attention in recent years. The association mining is widely adopted in E-commence applications.
Accounting to association rule is using attributes to generate a rule which containing relating itemsets are frequent itemsets in the transactions. The key point of getting better performance in association mining is how to choose a right way to generate frequent itemsets. The previous approaches have worse performance because of rescanning database,resulting in more I/O time. Beside this problem, the process also need all candidate itemsets to be saved in the memory and lots of computing operations to combine the frequent itemsets.
We proposed a Boolean-based expression approach for association mining. The attribute grouping is achieved by simply manipulating the boolean expression. Generating frequent itemsets within each group can reduce computing and memory space significantly. The simulation results show that our method outperforms the previous ones.

第一章, 簡介資料挖掘技術以及研究的動機與目的
第二章, 回顧相關的研究。
第三章, 敘述本研究之問題及基本構想,並且描述及分析利用屬性分群改善關聯式規則的方法。
第四章, 描述實驗及數據,並分析實驗結果。
第五章, 為本研究之結論與未來研究方向。

參考文獻
[1] R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proc. 20th Very Large Data Base Conf., Sept. 1994.
[2] S. J. Yen and A.L.P. Chen, “An Efficient Approach to Discovering Knowledge from Large Databases,” Fourth Int'l Conf. Parallel and Distributed Information Systems, Dec. 1996.
[3] M.J. Zaki, ”Scalable algorithms for association mining,” Knowledge and Data Engineering, IEEE Transactions on , vol 12, pp. 372 —390, May-June 2000
[4] J.S. Park, M. Chen, and P.S. Yu, “An Effective Hash Based Algorithm for Mining Association Rules,” ACM SIGMOD Int'l Conf. Management of Data, May 1995.
[5] A. Savasere, E. Omiecinski, and S. Navathe, “An Efficient Algorithm for Mining Association Rules in Large Databases,” Proc. 21st Very Large Data Base Conf., 1995.
[6] S.Y. Wur and Y. Leu, ”An Effective Boolean Algorithm for Mining Association Rules in Large Databases,” 6th International Conference on Database System for Advanced Applications(DASFAA),pp. 19-21, Hsinchu, Taiwan, ROC, April 1999
[7] R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. Inkeri Verkamo, “Fast Discovery of Association Rules,”Advances in Knowledge Discovery and Data Mining, U. Fayyad and et al., eds., pp. 307-328, Menlo Park, Calif.: AAAI Press, 1996.
[8] S. Brin, R. Motwani, J. Ullman, and S. Tsur, “Dynamic Itemset Counting and Implication Rules for Market Basket Data,” ACM SIGMOD Conf. Management of Data, May 1997.
[9] H. Toivonen, “Sampling Large Databases for Association Rules,” Proc. 22nd Very Large Data Bases Conf., 1996.
[10] R. Agrawal and J. Shafer, “Parallel Mining of Association Rules,” IEEE Trans. Knowledge and Data Eng., vol. 8, no. 6, pp. 962-969, Dec. 1996.
[11] D. Cheung, J. Han, V. Ng, A. Fu, and Y. Fu, “A Fast Distributed Algorithm for Mining Association Rules,” Fourth Int'l Conf. Parallel and Distributed Information Systems, Dec. 1996.
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[13] M.J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li, “Parallel Algorithms for Fast Discovery of Association Rules,” Data Mining and Knowledge Discovery: An Int'l Journal, vol. 1, no. 4, pp.343-373,Dec. 1997.
[14] H. Toivonen, “Sampling Large Databases for Association Rules,” Proc. 22nd Very Large Data Bases Conf., 1996.
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[16] G.D. Milligan and D.D. Corneil, “Corrections to Bierstone’s Algorithm for Gen-erating Cliques,” J.ACM,vol. 19,no. 2,pp. 244-247, 1972
[17] H. L. Chen and N. F. Tzeng, ”Subcube determination in faulty hypercubes,” Computers, IEEE Transactions on , vol 46 no. 8 , pp. 871 —879, Aug. 1997
[18] H. L. Chen and N. F. Tzeng, “A Boolean expression-based approach for maximum incomplete subcube identification in faulty hypercubes, ” Parallel and Distributed Systems, IEEE Transactions on , vol 8, no. 11 , pp. 1171 —1183, Nov. 1997

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