Abstract: Euclid distance is commonly used to measure distance in clustering analysis algorithm. The clustering analysis based on weighted Euclid distance is researched and presented to overcome the existing problems of similarity calculation in clustering analysis based on traditional Euclid distance when we have no any domain knowledge about the data objects. The experiment shows that the method has not only to certain extent overcome limitation of Euclid distance, but also been improving the clustering quality and optimizing performance.

Key words: weighted Euclid distance, compound correlation coefficient, clustering analysis, weight

摘要： 聚类分析中最常用的距离度量方法是欧氏距离。针对传统的基于欧氏距离计算相似度的不足，提出了一种在领域知识未知的情况下基于加权欧氏距离的计算方法，并对此进行了分析与研究。实验证明，该方法不仅在一定程度上克服了欧氏距离的缺陷，而且能够提高聚类质量，优化聚类性能。

关键词: 加权欧氏距离, 复相关系数, 聚类分析, 权重