English  |  正體中文  |  简体中文  |  Post-Print筆數 : 20 |  Items with full text/Total items : 90058/119991 (75%)
Visitors : 24086860      Online Users : 1247
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 理學院 > 資訊科學系 > 學位論文 >  Item 140.119/32638
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/32638


    Title: 高效率常見超集合探勘演算法之研究
    Efficient Algorithms for the Discovery of Frequent Superset
    Authors: 廖忠訓
    Liao, Zhung-Xun
    Contributors: 沈錳坤
    Shan, Man-Kwan
    廖忠訓
    Liao, Zhung-Xun
    Keywords: 常見超集合探勘
    常見樣式探勘
    關聯法則
    資料探勘
    Frequent Superset Mining
    Frequent Pattern Mining
    Association Rule
    Data Mining
    Date: 2004
    Issue Date: 2009-09-17 13:54:33 (UTC+8)
    Abstract: 過去對於探勘常見項目集的研究僅限於找出資料庫中交易紀錄的子集合,在這篇論文中,我們提出一個新的探勘主題:常見超集合探勘。常見超集合意指它包含資料庫中各筆紀錄的筆數多於最小門檻值,而原本用來探勘常見子集合的演算法並無法直接套用,因此我們以補集合的角度,提出了三個快速的演算法來解決這個新的問題。首先為Apriori-C:此為使用先廣後深搜尋的演算法,並且以掃描資料庫的方式來決定具有相同長度之候選超集合的支持度,第二個方法是Eclat-C:此為採用先深後廣搜尋的演算法,並且搭配交集法來計算倏選超集合的支持度,最後是DCT:此方法可利用過去常見子集合探勘的演算法來進行探勘,如此可以省下開發新系統的成本。
    常見超集合的探勘可以應用在電子化的遠距學習系統,生物資訊及工作排程的問題上。尤其在線上學習系統,我們可以利用常見超集合來代表一群學生的學習行為,並且藉以預測學生的學習成就,使得老師可以及時發現學生的學習迷失等行為;此外,透過常見超集合的探勘,我們也可以為學生推薦個人化的課程,以達到因材施教的教學目標。
    在實驗的部份,我們比較了各演算法的效率,並且分別改變實驗資料庫的下列四種變因:1) 交易資料的筆數、2) 每筆交易資料的平均長度、3) 資料庫中項目的總數和4) 最小門檻值。在最後的分析當中,可以清楚地看出我們提出的各種方法皆十分有效率並且具有可延伸性。
    The algorithms for the discovery of frequent itemset have been investigated widely. These frequent itemsets are subsets of database. In this thesis, we propose a novel mining task: mining frequent superset from the database of itemsets that is useful in bioinformatics, E-learning systems, jobshop scheduling, and so on. A frequent superset means that the number of transactions contained in it is not less than minimum support threshold. Intuitively, according to the Apriori algorithm, the level-wise discovering starts from 1-itemset, 2-itemset, and so forth. However, such steps cannot utilize the property of Apriori to reduce search space, because if an itemset is not frequent, its superset maybe frequent. In order to solve this problem, we propose three methods. The first is the Apriori-based approach, called Apriori-C. The second is the Eclat-based approach, called Eclat-C, which is a depth-first approach. The last is the proposed data complement technique (DCT) that we utilize original frequent itemset mining approach to discover frequent superset.
    The experimental studies compare the performance of the proposed three methods by considering the effect of the number of transactions, the average length of transactions, the number of different items, and minimum support. The analysis shows that the proposed algorithms are time efficient and scalable.
    Reference: [1] R. Agrawal, T. Imielinski, and A. Swami, “Mining Association Rules between Sets of Items in Large Databases,” Proc. of ACM Special Interest Group on Management of Data, ACM SIGMOD’93, 1993.
    [2] R. Agrawal and R. Srikant, ”Fast Algorithms for Mining Association Rules,” Proc. of the 20th International Conference on Very Large Data Bases, VLDB’94, 1994.
    [3] J. Han, J. Pei, and Y. Yin, “Mining Frequent Patterns without Candidate Generation,” Proc. of ACM Special Interest Group on Management of Data, ACM SIGMOD’00, 2000.
    [4] M. Zaki, S. Parthasarathy, M. Ogihara, and W. Li, “New Algorithms for Fast Discovery of Association Rules,” Proc. of the 3rd ACM Special Interest Group on Knowledge Discovery in Data and Data Mining, ACM SIGKDD’97, 1997.
    [5] R. Agarwal, C. Aggarwal, and V. Prasad, “A Tree Projection Algorithm for Generation of Frequent Itemsets,” Journal on Parallel and Distributed Computing (Special Issue on High Performance Data Mining), Vol. 61, Issue 3, 2000.
    [6] J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang, “H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Database,” Proc. of the 1st IEEE International Conference on Data Mining, ICDM’01, 2001.
    [7] J. Park, M. Chen, and P. Yu, “An Effective Hash-Based Algorithm for Mining Association Rules,” Proc. of ACM Special Interest Group on Management of Data, ACM SIGMOD’95, 1995.
    [8] J. Han and M. Kamber, “Data Mining: Concepts and Techniques,” Morgan Kaufman Publishers, 2000.
    [9] J. Hipp, U. Guntzer, and G. Nakhaeizadeh, “Algorithms for Association Rule Mining – A General Survey and Comparison,” ACM SIGKDD Explorations, Vol. 2, Issue 1, 2000.
    [10] D. Burdick, M. Calimlim, and J. Gehrke, “MAFIA: A Maximum Frequent Itemset Algorithm for Transactional Databases,” Proc. of the 17th International Conference on Data Engineering, ICDE’01, 2001.
    [11] R. Agarwal, C. Aggarwal, and V. Prasad, “Depth First Generation of Long Patterns,” Proc. of ACM Special Interest Group on Knowledge Discovery in Data and Data Mining, ACM SIGKDD’00, 2000.
    [12] M. Holsheimer, M. Kersten, H. Mannila, and H. Toivonen, “A Perspective on Databases and Data Mining,” Proc. of the 1st International Conference on Knowledge Discovery and Data Mining, KDD’95, 1995.
    [13] G. Gardarin, P. Pucheral, and F. Wu, “Bitmap Based Algorithm for Mining Association Rules,” Proc. of Actes des journes Bases de Donnes Avances, 1998.
    [14] A. Savasere, E. Omiecinski, and S. Navathe, “An Efficient Algorithm for Mining Association Rules in Large Databases,” Proc. of the 21st International Conference on Very Large Data Bases, VLDB’95, 1995.
    [15] F. Bodon, “A Fast APRIORI Implementation,” Proc. of the 3rd IEEE International Conference on Data Mining, ICDM’03, Workshop on Frequent Itemset Mining Implementions, FIMI’03, 2003.
    [16] Synthetic Data Generation Code for Associations and Sequential Patterns. http://www. almaden.ibm.com/software/quest/Resources/index.shtml Intelligent Information System, IBM Almaden Research Center.
    [17] T. Urdan and C. Weggen, “Corporate E-learning: Exploring a New Frontier,” WR Hambrecht + Co, 2000.
    [18] R. Beasley and M. Waugh, “The Effects of Content-Structure Focusing on Learner Structural Knowledge Acquisition, Retention, and Disorientation in a Hypermedia Environment,” Journal of Research on Computing in Education, Vol. 28, Issue 3, 1996.
    [19] R. Beasley and M. Waugh, “Cognitive Mapping Architectures and Hypermedia Disorientation: An Empirical Study,” Journal of Educational Multimedia and Hypermedia, Vol. 4, Issue 2-3, 1995.
    [20] C. Chang, “Cognitive Problem and Suggestions in Information Space Navigation–An Introduction and Survey,” Hua Fan Annual Journal, Vol. 4, No. 1, 1997.
    [21] G. Chen, C. Liu, K. Ou, and M. Lin, “Web Learning Portfolios: A Tool for Supporting Performance Awareness,” Innovations in Education and Training International, IETI, Vol. 38, Issue 1, 2000.
    [22] C. Liu, G. Chen, K. Ou, B. Liu, and J. Horng, "Managing Activity Dynamics of Web Based Collaborative Applications," International Journal of Artificial Intelligence Tools, JAIT, Vol. 8, Issue 2, 1999.
    [23] D. Wang, Y. Bao, G. Yu, and G. Wang, “Using Page Classification and Association Rule mining for Personalized recommendation in distance learning,” Proc. of the 1st International Conference on Web-based learning, ICWL’02, 2002.
    [24] D. Chiu, Y. Wu, and A. Chen, “An Efficient Algorithm for Mining Frequent Sequences by New Strategy without Support Counting,” Proc. of the 20th International Conference on Data Engineering, ICDE’03, 2003.
    [25] R. Agrawal and R. Srikant, “Mining Sequential Patterns,” Proc. of the 12th International Conference on Data Engineering, ICDE’95, 1995.
    [26] J. Setubal and J. Meidanis, Introduction to Computational Molecular Biology, PWS Publishing Company, 1997.
    [27] F. Gandon, “Ontology Engineering: a Survey and a Return on Experience,” Research Report of INRIA, RR4396, 2002.
    [28] N. Noy and M. Musen, “SMART: Automated Support for Ontology Merging and Alignment,” Proc. of the 12th Banff Workshop on Knowledge Acquisition, Modeling, and Management, KAW’99, 1999.
    [29] N. Noy and M. Musen, “PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment,” Proc. of the 7th National Conference on Artificial Intelligence and 12th Conference on Innovative Applications of Artificial Intelligence, AAAI’00, 2000.
    [30] G. Stumme and A. Maedche, “FCA-MERGE: Bottom-Up Merging of Ontologies,” Proc. of the 17th Internation Joint Conference on Artificial Intelligence, IJCAI’01, 2001.
    [31] S. Itoga, “The String Merging Problem,” BIT, Vol. 21, No. 1, 1981.
    Description: 碩士
    國立政治大學
    資訊科學學系
    91753013
    93
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0091753013
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

    Files in This Item:

    File Description SizeFormat
    75301301.pdf74KbAdobe PDF931View/Open
    75301302.pdf101KbAdobe PDF1320View/Open
    75301303.pdf115KbAdobe PDF884View/Open
    75301304.pdf43KbAdobe PDF827View/Open
    75301305.pdf43KbAdobe PDF867View/Open
    75301306.pdf45KbAdobe PDF862View/Open
    75301307.pdf58KbAdobe PDF946View/Open
    75301308.pdf202KbAdobe PDF2602View/Open
    75301309.pdf162KbAdobe PDF909View/Open
    75301310.pdf110KbAdobe PDF825View/Open
    75301311.pdf64KbAdobe PDF806View/Open
    75301312.pdf46KbAdobe PDF815View/Open
    75301313.pdf50KbAdobe PDF949View/Open
    75301314.pdf42KbAdobe PDF733View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback