基于数据挖掘的快速记录存储器数据处理技术

QAR data processing based on data mining

摘要:

针对目前国内缺少专门分析快速记录存储器(QAR)数据的有效手段的情况，研究了一种新的基于数据挖掘的QAR数据的分析方法。首先结合聚类和概率分析对k-means算法进行改进，解决了聚类数目难以确定的难题，形成了良好的聚类效果；然后，在此基础上结合加权最小距离分类器及概率分析的方法，对待分类的QAR数据的类别属性进行判断以确定异常数据；最后给出了仿真实验，验证了该方法的可行性和有效性。

Abstract:

According to the lack of efficient analysis tool for Quick Access Recorder(QAR) data, an improved data mining method is proposed in this paper. First, a modified algorithm of k-means based on probability theory is given. Then the cluster number of QAR data set is determined, so that better cluster results can be obtained. In order to identify the atypical data and the class of typical data, a weighted minimum distance classification as well as probability analysis is used. At last, experiments of cluster and classification are given to indicate the feasibility and effectiveness of the new method.