Comment on "Clustering by fast search and find of density peaks"

TL;DR

This paper proposes an automatic method to determine the threshold value for the density-based clustering algorithm by utilizing potential entropy, improving its accuracy and objectivity over subjective estimation.

Contribution

It introduces a novel approach to automatically select the threshold d-c using potential entropy, enhancing the original clustering method’s reliability.

Findings

The proposed method effectively determines the threshold d-c from data.

Experimental results match previous studies but with improved threshold selection.

The method reduces subjective bias in clustering parameter selection.

Abstract

In [1], a clustering algorithm was given to find the centers of clusters quickly. However, the accuracy of this algorithm heavily depend on the threshold value of d-c. Furthermore, [1] has not provided any efficient way to select the threshold value of d-c, that is, one can have to estimate the value of d_c depend on one's subjective experience. In this paper, based on the data field [2], we propose a new way to automatically extract the threshold value of d_c from the original data set by using the potential entropy of data field. For any data set to be clustered, the most reasonable value of d_c can be objectively calculated from the data set by using our proposed method. The same experiments in [1] are redone with our proposed method on the same experimental data set used in [1], the results of which shows that the problem to calculate the threshold value of d_c in [1] has been solved by using our method.