Clustering analysis elicits the natural groupings of a dataset without requiring information about the sample class and has been widely used in various fields. Although numerous clustering algorithms have been proposed and proven to perform reasonably well, no consensus exists about which one performs best in real situations. In this study, we propose a nonparametric clustering method based on recursive binary partitioning that was implemented in a classification and regression tree model. The proposed clustering algorithm has two key advantages: (1) users do not have to specify any parameters before running it; (2) the final clustering result is represented by a set of if–then rules, thereby facilitating analysis of the clustering results. Experiments with the simulations and real datasets demonstrate the effectiveness and usefulness of the proposed algorithm.
- Clustering algorithm
- Recursive binary partitioning
- Silhouette statistic
- Unsupervised learning
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Vision and Pattern Recognition