Density-based Clustering using Automatic Density Peak Detection
Huanqian Yan, Yonggang Lu, Heng Ma · 2018
Clustering is an important unsupervised machine learning method which has played an important role in various fields. Density-based clustering methods are capable of dealing with clusters of different sizes and shapes. As suggested by Alex Rodriguez et al. in a paper published in Science in 2014, the 2D decision graph of the estimated density value versus the minimum distance from the points with higher density values for all the data points can be used to identify the cluster centroids. However, there lack automatic methods for the determination of the cluster centroids from the decision graph. In this work, a novel statistic-based method is designed to identify the cluster centroids automatically from the decision graph. So the number of clusters is also automatically determined. Experiments on several synthetic and real-world datasets show the superiority of the proposed method in centroid identification from the datasets with various distributions and dimensionalities. Furthermore, it is also shown that the proposed method can be effectively applied to image segmentation. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.