关键词: 聚类, 混合型数据, 密度峰值, 属性编码, 海明度量

Abstract:

Clustering mixed data is usually evaluated according to the difference of sample attribute categories. However, this way of dividing the sample attributes into different subspaces separately separates the original unity of the sample attributes, and leads to the non-consistent metric deviation for the similarity evaluation of the sample individual. Concerning this issue, a new clustering algorithm based on binary coded sample attributes is proposed, and then unified metrics for attribute coding are carried out by Hamming’s difference. The new algorithm avoids the cutting of sample attributes by performing similarity measures on mixed data within a unified framework. Based on this, it also assigns different weights based on the properties of different attributes and to evaluate the similarity between the samples. The experimental results show that the new algorithm can effectively cluster mixed data, and compared with other existing clustering algorithms, it shows better clustering accuracy and stability.

Key words: clustering, hybrid data, density peak, attribute coding, Hamming metric