摘要
提出了一种基于四叉树K-均值聚类算法的软件故障预测算法。采用四叉树的目的包括利用四叉树寻找K-均值聚类算法所需要的聚类中心和利用四叉树来进行软件模块的故障预测。在这种算法中,输入门限参数决定了最初的聚类中心,通过改变门限参数,用户可以得到期望的聚类中心。采用了聚类收益这个新的标准来衡量算法的性能。通过仿真和比较,算法具有最高的聚类收益,且在大多数情况下,总体错误率比其他算法更低,从而表明了算法在软件故障预测中的有效性。
This paper applied a quad tree-based K-means algorithm for predicting faults in program modules. First, it applied quad trees for finding the initial cluster centers to be input to the K-means algorithm. An input threshold parameter governed the number of initial cluster centers and by varying the parameter, the user could generate desired initial cluster centers. It used the concept of clustering gain to determine the quality of clusters for evaluation of the quad tree-based initialization algo-rithm as compared to other initialization techniques. The clusters obtained by quad tree-based algorithm were found to have maximum gain values. Second, it applied the quad tree based algorithm for predicting faults in program modules. The overall error rates of this prediction approach are compared to other existing algorithms and are found to be better in most of the cases.
出处
《计算机应用研究》
CSCD
北大核心
2014年第9期2732-2735,共4页
Application Research of Computers
关键词
K-均值聚类
四叉树
软件故障预测
K-means clustering algorithm
quad tree
fault prediction
