基于孤立点和初始质心选择的k-均值改进算法

An Improved k-means Algorithm Based on Outliers and Original Clustering Center

介绍了在聚类中广泛应用的经典k-均值算法,针对其随机选择初始质心和易受孤立点的影响的不足,给出了一种改进的k-均值算法。首先使用距离法移除孤立点,然后采用邻近吸收法对初始质心的选择上进行了改进,并做了改进前后的对比试验。试验结果表明,改进后的算法比较稳定、准确,受孤立点和随机选择质心的影响也有所降低。

The classic algorithm of k-means was discussed,that was one of the most widespread methods in clustering,including both strongpoints and shortages.Not only is it sensitive to the original clustering center,but also it may be affected by the outliers.Given these shortages,an improved algorithm is discussed,which makes improvements in outliers and selection of original clustering center.The outlier detection is based on the distance method.To select original clustering center is assimilated based on the nearest neighbour.Experiment is checked,which indicates the improved one is more stable,more accurate.