基于Apriori算法的确定指定精度矩阵聚类方法  

Matrix clustering method achieving specific accuracy by modified Apriori algorithm

作　　者：陈立宁[1] 罗可[1] 

机构地区：[1]长沙理工大学计算机与通信工程学院,长沙410076

出　　处：《计算机工程与应用》2012年第7期139-141,共3页Computer Engineering and Applications

基　　金：国家自然科学基金(No.10926189;10871031);湖南省自然科学衡阳联合基金(No.10JJ8008);湖南省教育厅重点项目(No.10A015)

摘　　要：矩阵聚类法是一种对于给定稀疏二值矩阵求其相关指定面积和密集度的方法。在客户关系管理领域里作为一种数据挖掘技术,矩阵聚类法可以将相关客户和信息聚集成簇。在Apriori算法基础上加以改进提出一种新的矩阵聚类算法来获取满足具体指定条件的所有子矩阵。结果表明新算法能够具体细节地对客户的采购信息加以分析。Matrix clustering is defined as a method to obtain sub-matrices with specified area and density for the given sparse binary matrix. This method has been proposed as a data mining technique for customer relationship management and makes it possible to cluster the related items and customers. In this paper, the Apriori algorithm is extended and a new matrix clustering algorithm, which obtains all sub-matrices satisfying some specific condition is proposed. As a result, it is possible to analyze the purchase information of the customers in detail.

关 键 词：矩阵聚类 子矩阵 指定精度 

分 类 号：TP311.11[自动化与计算机技术—计算机软件与理论]