A clustering algorithm for categorical variables based on connected components
Zhou Hong-fan · Kongzhi yu juece · 2015
For the insufficient similarity concepts for categorical variables, a new more reasonable concept is proposed.Firstly, a data set is organized into an undirected graph by the new definition. The clustering process is converted into the problem of determining connected components in the undirected graph. Then a novel clustering algorithm for categorical variables based on connected components is proposed. In order to analyze the clustering results quantitatively, a new index is proposed for the known labels. Finally, the experimental results show that the proposed algorithm has a higher clustering precision and faster execution speed compared with several existing ones.