基于k-prototypes的混合属性数据聚类算法

通过对基于K-prototypes算法对混合属性数据处理的聚类问题进行研究，改进了K-prototypes算法中分类属性相异度计算公式，使之能更加精确反映样本间的差异；在此基础上提出了一种用于处理混合属性数据的聚类算法，并将改进后的算法应用于英语借词数据的聚类分析中。实验结果表明，与K-prototypes算法相比，改进后的算法具有更好的稳定性和更高的精度。

K-prototypes based clustering algorithm for data mixed with numeric and categorical values

Based on the K-prototypes, the clustering problem for data mixed with numeric and categorical values was researched in this paper. At first, an improved formula for computing the dissimilarity degree was proposed, compared with the formula in the K-prototypes algorithm. The modified formula can reflect the samples similarities and differences more precisely. Furthermore, a new clustering algorithm for data mixed with numeric and categorical values was presented on the basis of the improved formula for computing the dissimilarity degree, which was finally applied in the clustering analysis of English loanwords. The experimental results show that the new algorithm has better stability and higher precision than the traditional K-prototypes algorithm.