基于CURE聚类优化的数据挖掘算法研究

Research on data mining algorithm based on CURE clustering optimization

以海量非结构化数据为研究对象,在已有聚类算法研究基础上,从数据的获取与预处理,到数据聚类,再到数据的个性化管理全过程进行分析,针对数据聚类过程中的计算量庞大和复杂程度高的现状对已有CURE聚类算法进行优化,尽可能的最大程度提高数据聚类效率,进而提高大数据挖掘水平,为大数据应用提供更好的服务.通过以某通信企业不断变化的用户数据为分析对象,证明此次基于CURE聚类算法优化的数据挖掘算法有效性,结果表明CURE聚类算法优化具有一定的可用性和实用性.

This paper took massive unstructured data as the research object,and on the basis of existing clustering algorithm,from the data acquisition and preprocessing to data clustering,and the personalized management of data,the whole process was analyzed. Being aim at the present situation of the large amount of computation and complexity in the process of data clustering,this paper optimized the existing CURE clustering algorithm,as much as possible improve data clustering efficiency,and provided better services for data applications. Through user data in a communication enterprise changing as the analysis object,the effectiveness of data mining algorithm based on CURE clustering algorithm was proved,the results showed that CURE clustering algorithm had the availability and usefulness.