A categorical data clustering framework on graph representation. (August 2022) - Bai, Liang; Liang, Jiye
Highlights: Present a new converted-based clustering framework for categorical data. Propose a graph-based representation method of categorical data. The representation can sufficiently reflect similarity of categorical values. Show the effectiveness of the new framework in the experiments. Abstract: Clustering categorical data is an important task of machine learning, since the type of data widely exists in real world. However, the lack of an inherent order on the domains of categorical features prevents most of classical clustering algorithms from being directly applied for the type of data. Therefore, it is very key issue to learn an appropriate representation of categorical data for the clustering task. In order to address this issue, we develop a categorical data clustering framework based on graph representation. In this framework, a graph-based representation method for categorical data is proposed, which learns the representation of categorical values from their similar graph to provide similar representations for similar categorical values. We compared the proposed framework with other representation methods for categorical data clustering on benchmark data sets. The experiment results illustrate the proposed framework is very effective, compared to other methods.