基于粒计算的Apriori算法及其在图书管理系统中的应用

粒计算作为一种新的信息和知识处理的方法近来已经被许多研究者所重视,以及在许多领域中的得到应用。本质上,粒计算能够表示存储在系统中的数据的语义信息,因此粒计算能作为用于探索数据性质的一种方法,如挖掘数据库中的关联规则。本文在分析经典Apriori算法的基础上,从信息粒的角度出发,提出基于粒计算生成k-频繁项目集算法。分析了对给定问题,当用粒计算模型求解时需要解决的几个基本问题。最后通过实例说明如何通过信息粒的二进制表示,并基于粒计算k-频繁项目集生成算法来获取隐藏在图书借还记录中的有关关联规则。可以看出该算法具有实际应用价值。

An Apriori algorithm based on granular computing and its application in Library management system

As an emerging research method to deal with information and knowledge processing, granular computing have recently re- ceived more attention by many researchers and applied in many areas. In essence, information granules can represent the semantics of data stored in the systems. So, a granular computing model can serve as a way to explore the properties of the data examined, such as discovering association rules from a database. In this paper, on the basis of analyzing the Apriori algorithm and from a per- spective of information granules and granular computing, a generating k- frequent itemsets algorithm based on granular computing is presented. For a given problem, a few of basic questions are analyzed when using granular computing to solve the problem. Finally, how to extract the association rules hidden in the information table of borrowing and returning books is illustrated by using a real world example. It is shown that this algorithm is effective in practical applying domain.