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研究生:劉思吟
研究生(外文):Szu-Yin Liu
論文名稱:在串流環境下探勘不確定性資料
論文名稱(外文):Mining Frequent Patterns from Uncertain Data in Data Stream Environment
指導教授:李官陵
指導教授(外文):Guanling Lee
學位類別:碩士
校院名稱:國立東華大學
系所名稱:資訊工程學系
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:2012
畢業學年度:100
論文頁數:37
中文關鍵詞:頻繁項目集不確定性資料資料串流
外文關鍵詞:frequent patternuncertain datadata stream
相關次數:
  • 被引用被引用:0
  • 點閱點閱:147
  • 評分評分:
  • 下載下載:5
  • 收藏至我的研究室書目清單書目收藏:0
在過去,探勘頻繁項目集的許多研究中,它們的交易資料庫(transaction database)之數據大部分都是精確且靜止的,而在那些精確資料中,我們可以很容易的知道它的項目(item),但實際上,交易資料庫裡的資料有許多是不確定性的資料(uncertain data ),如在感測網路中所收集的資料,即為不確定性的資料,因此,不確定性的資料探勘,在近年來已成為新的研究焦點。除此之外,在感測網路與web應用等相關領域的資料分析中,資料是不斷的湧進,這樣的情況稱之為「資料串流(data stream)」。根據上述的特性:如何有效率且即時回應探勘之資料,是在資料探勘上的主要議題。因此,在本篇論文中,我們提出一個以「樹狀圖(tree)」為基礎的演算法,探討如何「在串流環境下探勘不確定性資料」。
In the conventional frequent itemsets mining researches, most of the data are precise and static in transaction databases. However, in real applications, such as the information collected in the sensor network, the data may be uncertain and continuous. Therefore, in recent years, mining uncertain data in a data stream environment has become an important research issue. In this thesis, we propose an efficient tree based algorithm to mine frequent uncertain itemsets in a data stream environment. Moreover, a set of experiment is performed to show the benefit of our approach.
致謝-------------------------------------------------------I
摘要------------------------------------------------------II
Abstract-------------------------------------------------III
圖目錄----------------------------------------------------V
表目錄----------------------------------------------------VII
第一章 緒論-----------------------------------------------1
第二章 背景知識與相關研究------------------------------------3
2.1背景知識-----------------------------------------------3
2.2相關研究--------- -------------------------------------5
2.2.1 在串流環境下的資料探勘---------------------------------5
2.2.2 在串流環境下探勘不確定性資料----------------------------6
第三章 主要演算法-------------------------------------------9
3.1 SUP演算法---------------------------------------------9
3.2 項目的結構--------------------------------------------20
3.3 演算法證明--------------------------------------------21
第四章 實驗結果--------------------------------------------23
第五章 結論與未來展望---------------------------------------33
參考資料--------------------------------------------------35
[1]Agrawal, R., Imielinski, T., Swami, Aurn.1993. Mining Association Rules Between Sets of Items in Large Databases. In: ACM SIGMOD Int. Conf. on Management of Data, pp.207–216 ,1993
[2]Arthur L. Corcorau and Sailclip Sen.1994.Using real-valued genetic algorithms to evolve rule sets for classification.In Proc. 1st IEEE Conf. Evolut. Computat., Orlando ,pp.120–124 ,1994
[3]Chui,C.-K.,Kao, B. and Hung, E.2007. Mining frequent itemsets from uncertain data, In Proc. PAKDD, pp. 47–58,2007.
[4]Chui,Chun Kit, Kao,Ben.2008.A decremental approach for mining frequent itemsets from uncertain data, In Proc.PAKDD, pp. 64–75,2008.
[5]Chang, J. H., Lee, W. S.2004.A Sliding Window Method for Finding Recently Frequent Itemsets over Online Data Streams.Journal of Information Science and Engineering, 2004.
[6]Das, G., Lin, K., Mannila, H., Renganathan, G. & Smyth, P.1988. Rule discovery from time series. In proceedings of the 4th Int'l Conference on Knowledge Discovery and Data Mining. pp 16-22,1988.
[7]Giannella,C., Han, J., Pei, J. , Yan,X. and Yu, P. S. 2003.Mining frequent patterns in data streams at multiple time granularities . In H. Kargupta, A. Joshi, K.Sivakumar, and Y. Yesha (eds.), Next Generation Data Mining, AAAI/MIT, pp.191-210,2003.
[8]Han, J., Pei, J., Yin, Y. 2000.Mining Frequent Patterns without Candidate Generation.In: ACM SIGMOD Int. Conf. on Management of Data, pp. 1–12 ,2000.
[9]Leung, Carson Kai-Sang, Carmichael, Christopher L.,Hao,Boyu.2007. Efficient mining of frequent patterns from uncertain data,In Proc. IEEE ICDM Workshops, pp. 489–494,2007.
[10]Leung,Carson Kai-Sang and Dale A Brajczuk.2009. Efficient algorithms for mining constrained frequent patterns from uncertain data. In Proc . U . pp. 9–18,2009.
[11]Li, Jinyan, Dong,Guozhu and Kotagiri Ramamohanarao.2000. Instance-based classification by emerging patterns. In Proc. PAKDD,pp.191–200,2000.
[12]Leung,Carson Kai-Sang and Hao, Boyu.2009. Mining of frequent itemsets from streams of uncertain data. In Proc. IEEE ICDE, pp. 1663–1670,2009.
[13]Lin,C. H.,Chiu,D.Y.,Wu,Y. H. and Chen, A. L. P.2005. Mining Frequent Itemsets from Data Streams with a Time-Sensitive Sliding Window. In Proc. SIAM , pp. 68-79,2005.
[14]Leung,Carson Kai-Sang, Hao, Boyu. and Jiang,Fan.2010. Constrained Frequent Itemset Mining from Uncertain Data Streams. In Proc. ICDE, pp. 120-127, 2010.
[15]Leung,Carson Kai-Sang, Jiang,F .2011. Frequent Pattern Mining from Time-Fading Streams of Uncertain Data. In Proc. DaWak, pp 252–264, 2011.
[16] Leung,Carson Kai-Sang,Jiang,Fan.2011.Frequent Itemset Mining of Uncertain Data Streams Using the Damped Window Model.In Proc. ACM SAC, pp.950-955,2011.
[17] Lakshmanan, Laks V.S.,Leung,Carson Kai-Sang,Ng,Raymond T..2003. Efficient dynamic mining of constrained frequent sets.ACM TODS, 28(4),Dec.2003, pp.337–389,2003
[18]Leung,Carson Kai-Sang , Mark Anthony F Mateo and Dale A Brajczuk.2008.A tree-based approach for frequent pattern mining fromuncertain data. In Proc. PAKDD, pp. 653–661,2008.
[19] Ng,Raymond T., Lakshmanan, Laks V.S., Han, Jiawei, Pang, Alex.1998. Exploratory mining and pruning optimizations of constrained associations rules. In Proc. ACM SIGMOD, pp. 13–24, 1998
[20]Thierry Denœux.2008. A k-nearest neighbor classification rule based on Dempster-Shafer theory, Studies in Fuzziness and Soft Computing, 2008, Volume 219/2008, pp. 737-760, 2008.
[21] Zhu,Y.,Shasha, D.2002.StartStream: Statistical Monitoring of Thousands of Data Streams in Real Time. In Proc. VLDB, pp. 358-369, 2002.
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