关键词: 聚类分析, 混合型数据, 数据场, 聚类趋势, 密度峰值

Abstract:

Focused on the issue that k-prototypes algorithm is incapable of identifying automatically number of clusters and discovering clusters with arbitrary shape, a mixed type data clustering algorithm based on searching for density peaks was proposed. Firstly, CFSFDP algorithm was extended to mixed data sets in which the distances between mixed data objects were calculated to determine the cluster centers according to the method of CFSFDP algorithm, that is, the number of clusters was determined automatically. The rest points were then assigned to the cluster in order of their density from large to small. Secondly, the selection method of threshold and weight in the proposed algorithm was introduced. In the density formula, the threshold (cutoff distance) was extracted automatically by calculating potential entropy of data field. In the distance formula, the weight was defined through certain statistic which can measure clustering tendency of numeric datasets and categorical datasets. Finally, after the testing on three real mixed datasets it is shown that compared with k-prototypes algorithm, the proposed algorithm can improve the accuracy of clustering

Key words: cluster analysis, mixed data, data field, clustering trend, density peaks