关键词: 聚类算法, 混合型分类数据, 距离度量, K-modes算法

Abstract: The K-modes algorithm is a traditional clustering technique, which uses a simple matching method to calculate the distance of different attribute values within one, while the weights of all attributes are the same. Taking this into account, the paper gives a new improved clustering algorithm. The new algorithm is more suitable for mixed categorical data by considering the sequential relation of attribute values in orderly categorical data, and the similarity between different attribute values in disordered categorical data and the relationship between attributes. The new algorithm deals with orderly categorical data and disordered categorical data by using different distance measurements. Moreover, the weights of attributes are given by average entropy. The experimental results show that the algorithm presented has better performance than the K-modes algorithm and its improved algorithm in both the artificial data set and the real data set.

Key words: clustering algorithm, mixed categorical data, distance metric, K-modes algorithm