Self-updating clustering algorithm for interval-valued data
Wen‐Liang Hung, Jenn-Hwai Yang, Kevin Shen · 2016
This paper proposes a robust automatic clustering algorithm based on the Hausdorff distance, called the self-updating clustering algorithm, for interval-valued data. This algorithm can simulate the self-clustering process. At the end of the clustering process, interval-valued data belonging to the same cluster converge to the same position, which represents the cluster's center. The numerical results show the effectiveness of the proposed algorithm using the overall error rate of classification (OERC) and the corrected rand (CR) index as criteria. An example of exoplanet data is also presented.