基于高校学生信息库的数据挖掘
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摘要
本文运用贝叶斯方法和关联规则对学生信息库进行数据挖掘，生成了对当前数据库有效的模型和关联规则，并对发现的规则进行分析，结合实际工作，为高校管理决策提供参考。
贝叶斯分类可以预测类成员关系的可能性，如给定样本属于一个特定类的概率。贝叶斯分类基于贝叶斯定理，朴素贝叶斯分类假定一个属性值对给定类的影响独立于其他属性的值。
关联规则发现算法的主要问题是通过怎样的算法找出所有强项集，然后找出有效关联规则。
We use the method of discovering Bayes Theorem and association rules to mine knolegde in student databases,the generated rules are valid in the current database ,we analyze the discovered rules and combine them with the practical work, which provide a scientific basis for college management and decision making.
Bayesian classifier are can predict class membership probabilities,such as the probability that a given sample belongs to particular class.Bayesian classification is based on Bayes theorem,decribed below.Nai've Bayesian classifiers assume that the effect of an attribute value on a given class is independent of the values of the other attributes.
The main problem of association rules found algorithmic is find all frequent itemsets,and generate strong association rules from the frequent itemsets.
贝叶斯分类可以预测类成员关系的可能性，如给定样本属于一个特定类的概率。贝叶斯分类基于贝叶斯定理，朴素贝叶斯分类假定一个属性值对给定类的影响独立于其他属性的值。
关联规则发现算法的主要问题是通过怎样的算法找出所有强项集，然后找出有效关联规则。
We use the method of discovering Bayes Theorem and association rules to mine knolegde in student databases,the generated rules are valid in the current database ,we analyze the discovered rules and combine them with the practical work, which provide a scientific basis for college management and decision making.
Bayesian classifier are can predict class membership probabilities,such as the probability that a given sample belongs to particular class.Bayesian classification is based on Bayes theorem,decribed below.Nai've Bayesian classifiers assume that the effect of an attribute value on a given class is independent of the values of the other attributes.
The main problem of association rules found algorithmic is find all frequent itemsets,and generate strong association rules from the frequent itemsets.
引文
【1】Ming-Syan Chen, Jiawei Han, Philip S yu. Data Mining : An Overview from a Database Perspective[J].IEEE Transactions on Knowledge and Data Engineering,1996,8(6):866-883.
【2】R Agrawal ,T1 mielinski, A Swami .Database Mining:A Performance Perspective[J]. IEEE Transactions on Knowledge and Data Engineering, 1993,12 : 914-925.
【3】Vasant Dhat. Data Mining In Finance :Using Counterfactuals to generate knowledge from organizational information system[J].Information System,1998,23(7):423-437.
【4】Rakesh Agrawal, Sakti Ghosh,Tomasz Imielinski, Bala lyer,,Arun Swami. An Interval Classifier for Database Mining Applications[M] .CLDB92. Vancouver ,British Coumbia, Vanada, 1992:560-573.
【5】J Han, Y Cai, N Cercone.Knowledge Discovery in Database : An Attribute-Oriented Approach[M].VLDB-92, Vancouver, British Columbia,Canada, 1992 : 547-559.
【6】G.Piatetsky-Shapiro,Knowledge Discovery in Databases,AAAI/MIT Press,1991
【7】王闯舟，打开数据仓库之门。微电脑世界周刊，2000(3-20)
【8】刘夫涛，数据仓库之我见。http://china3ifreewebsites.com
【9】R Agrawal,T Imielinski,A Swamai. Mining Association Rule between Sets of Items in Large Database[J],Proc .ACM SIGMOD, 1993,5:207-216
【10】R Agrawal,A Srikant. Fast Algorithms for Mining Association Rules in Large Database[J]. Proc. 20th int'1 Conf. Very Large Data Bases, 1994,9:478-499.
【11】J S Park,M S Chen ,P S YU. An Effective Hash Based Alogrithm for Mining Association Rules[J]. Proc. ACM SIGMOD, 1995,5:175-186.
【12】Ravi Krishnamurthy,Tomasz Imielinski.Practitioner Problems in need of database Research:Research Directions in knowledge Discovery[M].SIGMOD Record,1991,20(3):76-78.
【13】Harinarayan V, Ullman J D,Rajaram an A Impiementing data cubes efficiently, in:Procacm SIGMOD int Conf on Management of Data.Montreal, 1996
【14】Han J,Fu Y Exploration of the power of attribute-oriented induction in data mining.In:Fattad U M,Piatetsky-Shapiro G, Smyth Petaleds.Advances in Knowledge Discovery and Data Mining.Califomia:AAAI/MIT Press, 1996
【15】Davidhand ,Heikki Mannila,Padhraic Smyth .Principles of Data Mining,2003:209-215
【16】Jiawei Han,Micheline Kamber .Data Mining concepts and Techniques,2001:196-201
【17】Agrawal R,Imielinski T, Swami A,Mining Association Rule Between Sets of Items in Large Database .In:Bunemuu P, Jajodia S(eds),Proceedings of the 1993 ACM SIGMOD Conference on Management of Data,New York,NY:ACM Press, 1993:207-216
【18】Houtsma M,Swami A.Set-oriented Mining for Association Rules in Relational Databases. In:Yu P, Chen A(eds),Proceedings of the International Conference on Data Engineering,Los Alamitos,CA:IEEE Computer society Press, 1995:25-33
【19】Agrawal R,Srikant R,Fast Algorithms for Mining Association Rules in Large Databases.In Proceeding of the 20th International Conference on Very Large Database,Santiago,Chile, 1994-08-29-09-01
【20】Han J,Fu Y.Discovery of Multiple-level Association Rules from Large Database,In Proc.1995 Int.Conf.VLDB, 1995
【21】Savasere A,Omiecinski E,Navathe S.An Efficient Algorithm for Mining Association Rules in Large Databases. In:Proceedings of the 21st International Conference on Very Large Database,Zuich,Swizerland, 1995-09:432-444
【2】R Agrawal ,T1 mielinski, A Swami .Database Mining:A Performance Perspective[J]. IEEE Transactions on Knowledge and Data Engineering, 1993,12 : 914-925.
【3】Vasant Dhat. Data Mining In Finance :Using Counterfactuals to generate knowledge from organizational information system[J].Information System,1998,23(7):423-437.
【4】Rakesh Agrawal, Sakti Ghosh,Tomasz Imielinski, Bala lyer,,Arun Swami. An Interval Classifier for Database Mining Applications[M] .CLDB92. Vancouver ,British Coumbia, Vanada, 1992:560-573.
【5】J Han, Y Cai, N Cercone.Knowledge Discovery in Database : An Attribute-Oriented Approach[M].VLDB-92, Vancouver, British Columbia,Canada, 1992 : 547-559.
【6】G.Piatetsky-Shapiro,Knowledge Discovery in Databases,AAAI/MIT Press,1991
【7】王闯舟，打开数据仓库之门。微电脑世界周刊，2000(3-20)
【8】刘夫涛，数据仓库之我见。http://china3ifreewebsites.com
【9】R Agrawal,T Imielinski,A Swamai. Mining Association Rule between Sets of Items in Large Database[J],Proc .ACM SIGMOD, 1993,5:207-216
【10】R Agrawal,A Srikant. Fast Algorithms for Mining Association Rules in Large Database[J]. Proc. 20th int'1 Conf. Very Large Data Bases, 1994,9:478-499.
【11】J S Park,M S Chen ,P S YU. An Effective Hash Based Alogrithm for Mining Association Rules[J]. Proc. ACM SIGMOD, 1995,5:175-186.
【12】Ravi Krishnamurthy,Tomasz Imielinski.Practitioner Problems in need of database Research:Research Directions in knowledge Discovery[M].SIGMOD Record,1991,20(3):76-78.
【13】Harinarayan V, Ullman J D,Rajaram an A Impiementing data cubes efficiently, in:Procacm SIGMOD int Conf on Management of Data.Montreal, 1996
【14】Han J,Fu Y Exploration of the power of attribute-oriented induction in data mining.In:Fattad U M,Piatetsky-Shapiro G, Smyth Petaleds.Advances in Knowledge Discovery and Data Mining.Califomia:AAAI/MIT Press, 1996
【15】Davidhand ,Heikki Mannila,Padhraic Smyth .Principles of Data Mining,2003:209-215
【16】Jiawei Han,Micheline Kamber .Data Mining concepts and Techniques,2001:196-201
【17】Agrawal R,Imielinski T, Swami A,Mining Association Rule Between Sets of Items in Large Database .In:Bunemuu P, Jajodia S(eds),Proceedings of the 1993 ACM SIGMOD Conference on Management of Data,New York,NY:ACM Press, 1993:207-216
【18】Houtsma M,Swami A.Set-oriented Mining for Association Rules in Relational Databases. In:Yu P, Chen A(eds),Proceedings of the International Conference on Data Engineering,Los Alamitos,CA:IEEE Computer society Press, 1995:25-33
【19】Agrawal R,Srikant R,Fast Algorithms for Mining Association Rules in Large Databases.In Proceeding of the 20th International Conference on Very Large Database,Santiago,Chile, 1994-08-29-09-01
【20】Han J,Fu Y.Discovery of Multiple-level Association Rules from Large Database,In Proc.1995 Int.Conf.VLDB, 1995
【21】Savasere A,Omiecinski E,Navathe S.An Efficient Algorithm for Mining Association Rules in Large Databases. In:Proceedings of the 21st International Conference on Very Large Database,Zuich,Swizerland, 1995-09:432-444
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