A Global K-modes Algorithm for Clustering Categorical Data

作者：Bai Tian; Kulikowski C A; Gong Leiguang; Yang Bin; Huang Lan^*; Zhou Chunguang

来源：Chinese Journal of Electronics, 2012, 21(3): 460-465.

• Categorical data
• Clustering
• Data mining
• K-modes algorithm

In this paper, a new Global k-modes (GKM) algorithm is proposed for clustering categorical data. The new method randomly selects a sufficiently large number of initial modes to account for the global distribution of the data set, and then progressively eliminates the redundant modes using an iterative optimization process with an elimination criterion function. Systematic experiments were carried out with data from the UCI Machine learning repository. The results and a comparative evaluation show a high performance and consistency of the proposed method, which achieves significant improvement compared to other well-known k-modes-type algorithms in terms of clustering accuracy.

• 出版日期2012-7
• 单位吉林大学