关键词: 聚类分析, 分类型数据, 相异度度量, 结构相似性, k-modes算法

Abstract: Clustering is one of the important technology in data mining, which is based on similar principles to classify data. However, categorical data clustering is an important and difficult issue among many learning algorithms. The traditional k-modes algorithm uses a simple 0-1 matching method to define dissimilarity between two attribute values, does not take the distribution of the entire data set into account, which results in inaccurate measurement differences. Aiming at this problem, a k-modes algorithm based on structure similarity is proposed. The algorithm not only considers the attribute values of their own similarities and differences, but also considers the structure of them in other attributes. The simulation results from two aspects of cluster identification and accuracy show that the k-modes algorithm based on structure similarity is more effective in scalability and accuracy.

Key words: cluster analysis, categorical data, dissimilarity measure, structural similarity, k-modes algorithm