DenMune: Density peak based clustering using mutual nearest neighbors
作者：
Highlights：
• We present a novel algorithm (pseudo code given) to find clusters of arbitrary number, shapes and densities in two-dimensions. Higher dimensions are first reduced to 2-D using the t-sne algorithm.
• The algorithm relies on a single parameter K (the number of nearest neighbors).
• The algorithm proposes a simple rule that classifies the data points into three types: those that certainly belong to clusters/ certainly do not belong to any cluster (i.e. noise) and uncertain points (that either succeed to join a cluster or are considered, also, as noise).
• The performance of the proposed algorithm is compared to nine well known algorithms using thirty-six real and synthetic data sets.
• The results show the superiority of the proposed algorithm.
摘要
•We present a novel algorithm (pseudo code given) to find clusters of arbitrary number, shapes and densities in two-dimensions. Higher dimensions are first reduced to 2-D using the t-sne algorithm.•The algorithm relies on a single parameter K (the number of nearest neighbors).•The algorithm proposes a simple rule that classifies the data points into three types: those that certainly belong to clusters/ certainly do not belong to any cluster (i.e. noise) and uncertain points (that either succeed to join a cluster or are considered, also, as noise).•The performance of the proposed algorithm is compared to nine well known algorithms using thirty-six real and synthetic data sets.•The results show the superiority of the proposed algorithm.
论文关键词：Clustering,Mutual neighbors,Dimensionality reduction,Arbitrary shapes,Pattern recognition,Nearest neighbors,Density peak
论文评审过程：Received 25 October 2019, Revised 26 June 2020, Accepted 9 August 2020, Available online 11 August 2020, Version of Record 22 August 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107589
