【摘要】 介绍了在聚类中广泛应用的经典k-均值算法,并针对其易受随机选择初始聚类中心和孤立点的影响的不足,给出了改进的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聚类中心的选择进行了改进。并做了改进前后的对比实验和应用。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初始聚类中心的影响也有所降低。更多还原

【Abstract】 The classic algorithm of k-means is discussed,that is one of the most widespread methods in clustering,including both strongpoints and shortages.Not only it is sensitive to the original clustering center,but also it may be affectedby the outliers.Given these shortages,an improved algorithm is discussed,which makes improvements in outliers and selection of original clustering center.The outlier detection based on the distance method.To select original clustering center based on the nearest neighbour assimilated.Check experiment was done,which indicates the improved one is more stable,more accurate and the affection by the outliers is much low.更多还原