基于MMD聚类算法及在高校成绩分析中的应用

介绍了在聚类算法中广泛使用的k均值算法。针对其受选择初始质心和聚类个数影响的缺点,给出了改进的k均值算法。使用最大最小距离法选择初始聚类中心,并确定聚类个数。进行了改进前后的对比实验。实验结果表明,改进后的算法比较稳定、准确。将改进后的算法应用到高校成绩分析中,达到较好的分类效果。

Cluestering algorithm based on Max- min Distance for students' score analysis in universities and applications

The classic algorithm of k-means is discussed,that is one of the most widespread methods in clustering,including both strongpoint''s and shortages.Not only is it sensitive to the original clustering center,but also it may be affected by the k.Given these shortages,an improved algorithm is discussed,which makes improvements in k and selection of original clustering center.To select original clustering center based on the max-min distance.This paper presents the application which all show that the improved algorithm can lead to better and more stable solutions than k means algorithm.The experiment and application affection by the outliers is down to a much low figure.The improved algorithm was used to the students'' score analysis in universities and had a good closer.