【作者】 王媛媛；

【导师】 胡学钢；

【作者基本信息】 合肥工业大学 ， 计算机应用技术， 2005， 硕士

【摘要】 信息技术的日新月异使得各个领域的数据量激增,大型、分布式数据库成为数据存储的主要方式。在此背景下诞生的知识发现和数据挖掘给人们提供了一种新的认识数据、理解数据的智能手段,而概念格以它完备的结构和坚实的理论基础成为数据挖掘过程中的主要模型之一。 作为数据挖掘的一种重要模式,关联规则一直受到广泛的关注。本文提出了一种新的基于概念格模型挖掘关联规则的方法。该方法通过构造概念格来发现频繁项集,进而生成关联规则。当前大多数基于频繁项集求解关联规则的挖掘算法不仅需要多次扫描数据库,而且针对经常维护的数据库会增加重复的开销。本文提出的方法仅需一次扫描数据库即可确定最大频繁闭包项集并能够发现所有的频繁项集,节约了大量的I/O开销,提高了算法的时空性能。更为重要的是挖掘过程中构建的概念格是可维护的,利于动态数据库以及多种模式的挖掘。 文章首先讨论了概念格和扩展概念格的构造原理,提出了新的构造算法并给出两种有利于关联规则挖掘的剪枝概念格的构造方法。在此基础上详细描述了基于概念格模型的关联规则的挖掘算法,并以实验证明了算法的正确性和优越性。基于以上研究,文章最后提出了一种分布式挖掘关联规则的体系结构,该结构也能够处理大型数据库的关联规则挖掘,实验验证了算法的正确有效。更多还原

【Abstract】 The quick development of information technology leads an incredible rocket in all kinds of data. Large, distributed databases have been more and more popular. Just these cases bring birth to the KDD and Data Mining providing people a new approach to understand data. Due to a complete structure and solid mathematics theory, Concept Lattice has been accepted as the natural model of data mining.As an important pattern in data mining, association rules always attract many researchers. In this dissertation a new method is presented to mine association rules based on the model of concept lattice. By effectively building concept lattice, this method generates association rules after finding frequent itemsets. Taking into account that many current popular algorithms finding frequent itemsets not only need to scan databases lots of times, but also lead large amounts of unnecessary costs when dealing with those databases maintained from time to time. Our method only scans databases once. It can generate firstly the largest closed pattern and then get all frequent itemsets with good performance. What is more vital is that the concept lattice built during the mining process can be maintained easilier when databases change. Meanwhile, the concept lattice can be stored to mine other patterns.This dissertation distusses the principles of building concept lattice and reduced extended concept lattice, presents new algorithms to build them, and describes two methods to prune concept lattice for mining association rules. Based on these, the algorithm of finding association rules and the results of experiments are also shown in detail in this dissertation. At last, one kind of architecture of distributed mining association rules is presented. This architecture can also be used to mine very large databases and it is proved correct by experiments.更多还原