[BP算法应用.doc

摘要 今年来随着数据库和计算机网络的广泛应用，加上使用先进的自动数据生成和采集工具，人们所拥有的数据量急剧增加。数据迅速增加与数据分析方法的滞后之间的矛盾越来越突出，人们也希望能够在对已有的大量数据分析的基础之上进行科学研究、商业决策或者企业管理，但是目前所拥有的数据分析工具很难对数据进行深层次的处理，使得人民只能望数兴叹。数据挖掘正是为了解决传统分析方法的不足，并针对大规模数据的分析处理而出现的，数据挖掘从大量的数据中提取出隐藏在数据之后的有用的信息，它越来越多的领域所采用，并取得了较好的效果，为人们的正确决策提供了很大帮助。 本文着眼于数据挖掘的有关的算法，简单介绍了这些算法，并运用这些算法进行实际问题的求解。本文着重介绍的是关联规则的Apriori算法和神经网络中的BP算法。在介绍Apriori算法时，还运用Apriori算法求解当下高等学校排课的问题；在介绍BP算法时，用其解决了一个在政府投资项目的投资估算的问题。 Abstract This year, with the extensive application of database and computer network, and the use of advanced automatic data generation and collection tools, people have a sharp increase in the amount of data. Scientific research, business decisions or enterprise management data increase rapidly and data analysis method of the lag between the contradictions are becoming increasingly prominent, people also hope that in the analysis of the existing large amounts of data can be the basis of, but is currently owned by data analysis tool is difficult to the data were deep, making people have only a few look helplessly. Data mining is to solve the problem of the conventional method of analysis, and for large-scale data analysis and processing of data mining from large amounts of data to extract useful information hidden in the data, adopted by more and more areas and achieved good results, and provide a big help for people to make a correct decision. This paper focuses on the relevant algorithms of data mining, and simply introduces these algorithms, and uses these algorithms to solve real problem.. This paper mainly introduces the Apriori algorithm and BP algorithm in neural network.. In the Apriori algorithm, using the Apriori algorithm to solve the current higher school timetabling problem; BP algorithm is introduced, with its solves the A in government investment project investment estimation problem. 绪论 1.1 研究背景 就目前而言，关联规则挖掘技术已经被广泛应用在西方金融行业企业中，它可以成功预测银行客户需求。一旦获得了这些信息，银行就可以改善自身营销。现在银行天天都在开发新的沟通客户的方法。各银行在自己的ATM机上就捆绑了顾客可能感兴趣的本行产品信息，供使用本行ATM机的用户了解。如果数据库