关键词: 聚类, 混合型数据, 相似度计算, 层次聚类

Abstract: In many applications, many datasets have the features of both numeric and categorical data, the k-prototype is one of the most important algorithms designed for clustering this type data. Based on the studying of the existing clustering algorithms for mixed data, it proposes a new algorithm for the clustering of mixed data, not only modifies the method of the searching of prototypes, but also designs a new measurement of similarity to measure the similarity between data objects. It also proposes a new method that different from k-prototype to allocate data to prototype. It uses the idea similar to the agglomerate clustering in hierarchical clustering to clustering, after the testing on four real mixed datasets it is found that compared with other algorithms, the proposed algorithm not only can improve the accuracy of clustering, but also has the very high stability.

Key words: clustering, mixed data, similarity measure, hierarchical clustering