摘要
Clustering is essential in data analysis since many real-world datasets are unlabeled and are expensive to label. Density-based clustering algorithms are known for their capability of identifying clusters of non-spherical shapes and have been widely studied over recent decades. Among these algorithms, Density Peaks (DP) clustering is an outstanding one that is particularly robust to changes in the distance metric. However, the performance of DP clustering highly depends on the choice of its hyperparameters: the cutoff distance and the number of clusters. As a result, we developed a learning-based approach for selecting appropriate values of the hyperparameters for DP clustering in one-shot. We address the challenging issue of effective one-shot hyperparameter selection by proposing two novel embeddings: the HINT (Histogram of Neighborhood Transform) embedding and the Gamma embedding. The HINT embedding calculates the histogram of the $m$m-th neighborhood distances for each node. The neighborhood distances histogram captures practical characteristics of the density property for a given dataset. Meanwhile, the Gamma embedding condenses the information of a decision graph while still providing crucial clues for determining the number of clusters. Therefore, we achieved effective one-shot hyperparameter selection by the proposed novel embeddings. As compared with an exhaustive grid search method, our method is 169 times faster, while its relative performance ratio is up to 89.6%, which demonstrates its effectiveness. As there has been a shortage of research devoted to hyperparameter selection for DP clustering, we expected our promising result to inspire more studies toward the important research topic.