Comment on "Clustering by fast search and find of density peaks"
This is an incremental improvement for users of clustering algorithms, specifically targeting the parameter selection bottleneck in density peak clustering.
The paper addresses the problem of manually selecting the threshold parameter d_c in a clustering algorithm, which affects accuracy, by proposing an automatic method using potential entropy of data field to objectively calculate d_c from the dataset. The results show that this method solves the threshold calculation issue, as demonstrated by redoing experiments from the original work.
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.