聚类算法中的相似性度量方法研究

Research on Similarity Metrics Method for Clustering Algorithm

针对传统的欧氏距离计算相异度的不足,在研究已有的相似性度量方法的基础上提出一种新的相似性计算方法,对此进行分析,说明了该度量方法有更好的可解释性;把它用于k-means聚类算法中跟欧氏距离进行比较,在UCI基准数据集上的实验表明,该方法有更稳定的聚类结果,且提高了聚类准确率,是一种有效的聚类度量方法。

According to the disadvantages of calculating dissimilarity based on traditional Euclidean distance, presented a new similarity metrics method after studied the existing method of the similarity measure, Analysis showed that the metrics method can be better interpretative; Used it in the k-means clustering algorithm with Euclidean distance comparison, the experiments bases on UCI benchmark data sets showed that this method has more stable clustering results, and improved the accuracy of clustering, it is an effective clustering metrics.