An improved hierarchical clustering method based on the k‐NN and density peak clustering

Abstract Clustering is one of the most prevalent and important data mining algorithms ever developed. Currently, most clustering methods are divided into distance‐based and density‐based. In 2014, the fast search and find of density peaks clustering method was proposed, which is simple and effective and has been extensively applied in several research domains. However, the original version requires manually assigning a cut‐off distance and selecting core points. Therefore, this article improves the density peak clustering method from two aspects. First, the Gaussian kernel is substituted with a k‐nearest neighbors method to calculate local density. This is important as compared with selecting a cut‐off distance, calculating the k ‐value is easier. Second, the core points are automatically selected, unlike the original method that manually selects the core points regarding local density and distance distribution. Given that users' selection influences the clustering result, the proposed automatic core point selection strategy overcomes the human interference problem. Additionally, in the clustering process, the proposed method reduces the influence of manually assigned parameters.