研讨环境的研讨信息分析处理研究
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摘要
支持群体研讨并能达成共识是综合集成研讨厅的基本功能,近几年来,随着综合集成研讨厅体系逐渐的发展,人在思维方面得到了明显的提高,同时,数据挖掘技术也引起了信息产业界以及整个社会的的极大关注,关联规则和聚类技术是数据挖掘技术中两个重要的研究方向,本文以钱学森的“综合集成法”为依据,通过设计与实现研讨式教学系统,提供一个实时交互、实时查阅、实时记录的研讨环境,探索问题研讨的新方法,并对研讨环境的研讨信息进行分析处理研究,阐述了聚类分析和关联规则的基本概念,其中简单描述了Aprioritid算法,比较了Aprioritid算法与Apriori算法的执行时间,提出了改进的K-Means聚类算法,将关联规则算法应用在聚类分析的结果中,以实现关联关系的挖掘,主要研究工作如下:
(1)讨论了协商研讨环境中所使用到的Toulmin模式的研讨模型,对研讨信息进行了相应的分析处理,分析了提案共识达成模型。
(2)设计并实现了研讨式教学系统,对系统的需求进行了分析,设计了系统数据库,另外介绍了系统主要功能模块的实现。
(3)分析研讨环境的研讨信息,将关联规则应用在研讨式教学系统中,用研讨式教学系统作为平台进行研究,首先对研讨信息进行聚类分析,然后使用Aprioritid算法研究学员提出的各种主张之间的关联关系,找出学员一致赞同的结果,并且对整个过程进行实现。
依据理论与实践相结合的方法,用MyEclipse作为开发平台,以提案共识达成模型为理论基础,设计并实现了教学系统的基本功能模块,并且通过对学员在研讨课上提出的观点进行挖掘,详细分析了学员提出的观点的关联关系,验证了理论的合理性和可行性。
The basic functions of Hall for Workshop of Metasynthetic Engineering is support group discussion and reach a consensus, in recent years, with the system of Hall for Workshop of Metasynthetic Engineering gradually development, the aspects of thought has been significantly improved, while the technology of data mining has caused great attention of the information industry and the whole society, two important research direction of data mining technology are association rules and clustering technology, in the basis of Qian's "comprehensive integrated method" in this paper, through the design and implementation of seminar teaching system to provide a real-time interactive, real-time check, real-time record discussion environment, explore new methods of the discussion of the issue, and analyzed and researched the discussion information of discussion environment, elaborated basic concepts of the cluster analysis and association rules, including a brief description of the Aprioritid algorithm, compared with the execution time of the Apriori algorithm and the Aprioritid algorithm, proposed an improved K-Means clustering algorithm, association rule algorithm will be applied to the results of cluster analysis in order to achieve the mining association, the main research work is as follows:
(1) Discussed the discussion models of the Toulmin model in the negotiation seminar environment, analyzed and researched the discussion information, analyzed a model to reach proposal consensus.
(2) To participate in the design and implementation of the seminar-style teaching system, analyzed the needs of the system, work out the system database, and also introduces the major functional modules of the system
(3)Analyzed the seminar information of the discussion environmental, used the association rules in the teaching system, teaching system used as a platform to discuss research, cluster analysis on the discussion information first, and then research various relationship between ideas to the students with Aprioritid algorithms, identify the unanimously endorsed results to participants and the whole process of implementation.
Based on the method of the combination of theory and practice, using MyEclipse as a development platform , use a consensus model of the proposal as the theory, involved in the design and implementation of the basic functions modules of teaching system, and mining the viewpoint on the seminar proposed by the participants, analyzed the relationship of the student's point in detailed, proved the rationality and feasibility of the theory.
(1)讨论了协商研讨环境中所使用到的Toulmin模式的研讨模型,对研讨信息进行了相应的分析处理,分析了提案共识达成模型。
(2)设计并实现了研讨式教学系统,对系统的需求进行了分析,设计了系统数据库,另外介绍了系统主要功能模块的实现。
(3)分析研讨环境的研讨信息,将关联规则应用在研讨式教学系统中,用研讨式教学系统作为平台进行研究,首先对研讨信息进行聚类分析,然后使用Aprioritid算法研究学员提出的各种主张之间的关联关系,找出学员一致赞同的结果,并且对整个过程进行实现。
依据理论与实践相结合的方法,用MyEclipse作为开发平台,以提案共识达成模型为理论基础,设计并实现了教学系统的基本功能模块,并且通过对学员在研讨课上提出的观点进行挖掘,详细分析了学员提出的观点的关联关系,验证了理论的合理性和可行性。
The basic functions of Hall for Workshop of Metasynthetic Engineering is support group discussion and reach a consensus, in recent years, with the system of Hall for Workshop of Metasynthetic Engineering gradually development, the aspects of thought has been significantly improved, while the technology of data mining has caused great attention of the information industry and the whole society, two important research direction of data mining technology are association rules and clustering technology, in the basis of Qian's "comprehensive integrated method" in this paper, through the design and implementation of seminar teaching system to provide a real-time interactive, real-time check, real-time record discussion environment, explore new methods of the discussion of the issue, and analyzed and researched the discussion information of discussion environment, elaborated basic concepts of the cluster analysis and association rules, including a brief description of the Aprioritid algorithm, compared with the execution time of the Apriori algorithm and the Aprioritid algorithm, proposed an improved K-Means clustering algorithm, association rule algorithm will be applied to the results of cluster analysis in order to achieve the mining association, the main research work is as follows:
(1) Discussed the discussion models of the Toulmin model in the negotiation seminar environment, analyzed and researched the discussion information, analyzed a model to reach proposal consensus.
(2) To participate in the design and implementation of the seminar-style teaching system, analyzed the needs of the system, work out the system database, and also introduces the major functional modules of the system
(3)Analyzed the seminar information of the discussion environmental, used the association rules in the teaching system, teaching system used as a platform to discuss research, cluster analysis on the discussion information first, and then research various relationship between ideas to the students with Aprioritid algorithms, identify the unanimously endorsed results to participants and the whole process of implementation.
Based on the method of the combination of theory and practice, using MyEclipse as a development platform , use a consensus model of the proposal as the theory, involved in the design and implementation of the basic functions modules of teaching system, and mining the viewpoint on the seminar proposed by the participants, analyzed the relationship of the student's point in detailed, proved the rationality and feasibility of the theory.
引文
[1]于景元,钱学森.关于开放的复杂巨系统的研究[J].系统工程理论与实践,1992,12(5):8-12.
[2]钱学森,于景元,戴汝为.一个科学的新领域—开放的复杂巨系统及其方法论[J].自然杂志,1990,13(1):3-10.
[3]唐锡晋.对综合集成研究发展的认识和相关研究[J].军事运筹与系统工程, 2003,3:2-5.
[4]顾基发,王浣尘,唐锡晋等.综合集成方法体系与系统学研究[M].北京:科学出版社,2007, 1.
[5]陈安,陈宁,周龙骧等.数据挖掘技术及应用[M].北京:科学出版社,2006, 3.
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[7]唐锡晋,刘怡君.从群体支持系统到创造力支持系统[J].系统工程理论与实践2006,5:63-71.
[8]刘春梅,戴汝为.综合集成研讨厅专家群体评估结果的可视化[J].模式识别与人工智能, 2005.2
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[12]谭俊峰,张朋柱,黄丽宁.综合集成研讨厅中的研讨信息组织模型[J].系统工程理论与实践, 2005,25(1):86-92.
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[14]邢永康,招福新.关联规则算法研究及其在高校学生信息库挖掘中的应用[D][硕士论文].重庆大学,2005.
[15]熊才权,黄鹤,张玉.基于关联规则的研讨信息分析研究[J],计算机与数字工程,2009,1:37-41.
[16]熊才权,李德华.综合集成研讨厅共识达成模型及其实现[J].计算机集成制造系统,2008,14(10):1913-1918.
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[23]戴汝为.人机结合的智能工程系统—处理开放的复杂巨系统的可操作平台[J].模式识别与人工智能,2004,17(3):257-261.
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[30]LOW E D G. Cooprtative structuring of information the representation of reasoning and debate[J]. International journal of Man-Machine Studies,1985,23:97-111.
[31]Sotiris Kotsiantis, Dimitris Kanellopoulos. Association Rules Mining: A RecentOverview[J] , GESTS International Transactions on Computer Science and Engineering,2006,21:589-609.
[32]Trauth E M,Jessup L M.Understandig computer mediated discussios positives and interpretive analyses of group support system use[J]. MIS Quarterly, 2000, 24(1):43-79.
[33]John A.A. Sillince. Argumentation and contract models for strategic organization support systems [J]. Decision Support Systems,1996, 16: 255-274.
[34]Frame-based argumentation for group decision task generation and identification [J].Decision Support Systems,2005,39:643–659.
[35]Jyothsna R.Nayak and Diane J.Cook. Approximate Association Rule Mining. [J].Department of Computer Science and Engineering ,1998.
[36] Robert Godin, Rokia Missaoui. An incremental concept formation approach for learning from databases[J],Theoretical Computer Science,1994,10:175-186.
[37]Heikki Mannila.Hannu Toivonen.and A.Inkeri Verkamo.Effeicient Algorithms for Discovering Association Rules. Usama M.Fayyad and Ramasamy Uthurusamy. Proceedeings of AAAI’94 Workshop on Knowledge Discovery in Databases(KDD’94) Seattle, Washington, 1994.AAAI Press Publisher.1994:181-192.
[38]冯洁,陶宏才.典型关联规则挖掘算法的分析与比较[J].计算机技术与发展,2007,17(3):121-124.
[39]周娜,廖文和,杨浩等.基于分类和关联规则的个性化产品推荐系统[J].高技术通讯,2004,11:51-55.
[40]Chen H,Hsu P,Orwing R,etal.Automatice concept classification of text from electronic meetings[J].Association for Computer Machinery,Communications of the ACM,1994,37(10):56-73.
[41]谭俊峰,张朋柱,程少川等.面向研讨过程的群体成员权重算法[J].系统工程理论方法应用,2005,14(2):97-103.
[42]Dr. Abdallah Alashqur. Mining Association Rules: A Database Perspective[J] International Journal of Computer Science and Network Security, 2008:75-84.
[43] Asa Ben-Hur, David Horn, Hava T. Siegelmann. Support Vector Clustering [J],The Journal of Machine Learning Research,2002,328(12): 1391-1404.
[44]S.Brin,R.Motwani.J.D.UIIman.and S.Tsur.Dynamic Itemset counting and implication rules for market basket data.Joan Peckham.Proceedings of the 1997ACM-SIGMOD International Conference on Management of Data (SIGMOD’97). Tucson, Arizona. 1997.ACM Press Publisher.1997:255-264.
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[2]钱学森,于景元,戴汝为.一个科学的新领域—开放的复杂巨系统及其方法论[J].自然杂志,1990,13(1):3-10.
[3]唐锡晋.对综合集成研究发展的认识和相关研究[J].军事运筹与系统工程, 2003,3:2-5.
[4]顾基发,王浣尘,唐锡晋等.综合集成方法体系与系统学研究[M].北京:科学出版社,2007, 1.
[5]陈安,陈宁,周龙骧等.数据挖掘技术及应用[M].北京:科学出版社,2006, 3.
[6]朱玉全,杨鹤标,孙蕾等.数据挖掘技术[M].南京:东南大学出版社,2006, 11.
[7]唐锡晋,刘怡君.从群体支持系统到创造力支持系统[J].系统工程理论与实践2006,5:63-71.
[8]刘春梅,戴汝为.综合集成研讨厅专家群体评估结果的可视化[J].模式识别与人工智能, 2005.2
[9]于景元,周晓纪.从定性到定量综合集成方法的实现和应用[J].系统工程理论与实践,2002,22(10):26-32.
[10]唐锡晋,刘怡君.有关社会焦点问题的群体研讨实验—定性综合集成的一种实践[J].系统工程理论与实践, 2007,3:42-49.
[11]颜雪松,蔡之华.一种基于Apriori的高效关联规则挖掘算法的研究[J].计算机工程与应用,2002,10:209-211.
[12]谭俊峰,张朋柱,黄丽宁.综合集成研讨厅中的研讨信息组织模型[J].系统工程理论与实践, 2005,25(1):86-92.
[13]李德华,熊才权.一种研讨信息组织模型及其在研讨厅中的应用[J].计算机应用研究,2008, 9(25):2730-2733.
[14]邢永康,招福新.关联规则算法研究及其在高校学生信息库挖掘中的应用[D][硕士论文].重庆大学,2005.
[15]熊才权,黄鹤,张玉.基于关联规则的研讨信息分析研究[J],计算机与数字工程,2009,1:37-41.
[16]熊才权,李德华.综合集成研讨厅共识达成模型及其实现[J].计算机集成制造系统,2008,14(10):1913-1918.
[17]TOULMIN S E.The uses of argument[M].NewYork:Cambridge University, 1958.
[18]Pang-Ning Tan Michael Steinbach Vipin Kumar著,范明,范宏建等译.数据挖掘导论[M].北京:人民邮电出版社,2006,5.
[19]韩秋明,李薇,李华峰等.数据挖掘技术应用实例[M].北京:机械工业出版社,2009, 4.
[20]R Agrawal.Mining Association Rules Between Sets of Items in Large Databases[C]. Washington: Proceedings of the ACM SIGMOD International Conference Management of Data,1993:207-216.
[21]Rakesh Agrawal, Ramakrishnan Srikant. Fast Algorithms for Mining Association Rules[C],Proceedings of the 20th VLDB Conference,1994,1(2): 1-26.
[22]Jiawei Han,Jian Pei,and Yiwen Yin.Mining frequent patterns without candidate genernation.In Weidong Chen, Jeffrey F.Naughton,and Philip A.Bernstein, editors, Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data,Dallas.Texas,USA,May,ACM Press.2000:1-12.
[23]戴汝为.人机结合的智能工程系统—处理开放的复杂巨系统的可操作平台[J].模式识别与人工智能,2004,17(3):257-261.
[24] Herrera E Herrera-Viedma E,Verdegay J L. A rational consensus model in group decision making using linguistic assessments.Fuzzy Sets and Systems,1997(88):31-49.
[25]毛嘉莉.基于K-means的文本聚类算法[J].计算机系统应用, 2009,10:85-87.
[26]杨彩莲,谢福鼎.基于主题概念聚类的中文文本聚类[J].现代电子技术,2007,27:161-163.
[27] Asa Ben-Hur, David Horn, Hava T. Siegelmann. Support Vector Clustering [J],The Journal of Machine Learning Research,2002,328(12): 1391-1404.
[28]H.Toivonen.Sampling large databases for association rules.T.M.Vijayaraman Alejandro P.Buchmann.C.Mohan.and Nandlal L.Sarda.Proceedings of the 22nd International Conference on Very Large Databases(VLDB’96). Bombay.India.1996. Morgan Kaufmann Publisher,1996:134 -145.
[29]Qian xuesen,Yu Jingyuan,Dai Ruwei.A New Discipline of science:The Study of Open Complex Giant System and Its Methodology.Journal of Systems Engneering& Electr0nics,1993,4(2):2-12
[30]LOW E D G. Cooprtative structuring of information the representation of reasoning and debate[J]. International journal of Man-Machine Studies,1985,23:97-111.
[31]Sotiris Kotsiantis, Dimitris Kanellopoulos. Association Rules Mining: A RecentOverview[J] , GESTS International Transactions on Computer Science and Engineering,2006,21:589-609.
[32]Trauth E M,Jessup L M.Understandig computer mediated discussios positives and interpretive analyses of group support system use[J]. MIS Quarterly, 2000, 24(1):43-79.
[33]John A.A. Sillince. Argumentation and contract models for strategic organization support systems [J]. Decision Support Systems,1996, 16: 255-274.
[34]Frame-based argumentation for group decision task generation and identification [J].Decision Support Systems,2005,39:643–659.
[35]Jyothsna R.Nayak and Diane J.Cook. Approximate Association Rule Mining. [J].Department of Computer Science and Engineering ,1998.
[36] Robert Godin, Rokia Missaoui. An incremental concept formation approach for learning from databases[J],Theoretical Computer Science,1994,10:175-186.
[37]Heikki Mannila.Hannu Toivonen.and A.Inkeri Verkamo.Effeicient Algorithms for Discovering Association Rules. Usama M.Fayyad and Ramasamy Uthurusamy. Proceedeings of AAAI’94 Workshop on Knowledge Discovery in Databases(KDD’94) Seattle, Washington, 1994.AAAI Press Publisher.1994:181-192.
[38]冯洁,陶宏才.典型关联规则挖掘算法的分析与比较[J].计算机技术与发展,2007,17(3):121-124.
[39]周娜,廖文和,杨浩等.基于分类和关联规则的个性化产品推荐系统[J].高技术通讯,2004,11:51-55.
[40]Chen H,Hsu P,Orwing R,etal.Automatice concept classification of text from electronic meetings[J].Association for Computer Machinery,Communications of the ACM,1994,37(10):56-73.
[41]谭俊峰,张朋柱,程少川等.面向研讨过程的群体成员权重算法[J].系统工程理论方法应用,2005,14(2):97-103.
[42]Dr. Abdallah Alashqur. Mining Association Rules: A Database Perspective[J] International Journal of Computer Science and Network Security, 2008:75-84.
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