基于质心的样本加权聚类算法

Algorithm of Sample's Weighting Clustering Based on Centroid

针对传统的以k-means为代表的分割聚类算法认为所有的聚类样本对聚类中心的影响都是相同的这一观点,提出基于样本加权的聚类算法,并采用实际数据集验证算法的有效性.实验表明,该算法比传统的k-means聚类算法具有更高的精确度.

The traditional partitional clustering algorithm represented by k-means considers all the clustering samples have the same impacts on clustering center.In view of this point,a clustering algorithm was proposed to deal with different samples＇ weight and was applied to the evaluation of teaching quality.To evaluate the proposed algorithm,some real and artificial dataset were used to verify the effectiveness of the algorithm.The findings show that the proposed algorithm has higher precision than the traditional k-means clustering algorithm.