DenMune: Density peak based clustering using mutual nearest neighbors

Many clustering algorithms fail when clusters are of arbitrary shapes, of varying densities, or the data classes are unbalanced and close to each other, even in two dimensions. A novel clustering algorithm DenMune is presented to meet this challenge. It is based on identifying dense regions using mutual nearest neighborhoods of size K , where K is the only parameter required from the user, besides obeying the mutual nearest neighbor consistency principle. The algorithm is stable for a wide range of values of K . Moreover, it is able to automatically detect and remove noise from the clustering process as well as detecting the target clusters. It produces robust results on various low and high dimensional datasets relative to several known state of the art clustering algorithms. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

The novel clustering algorithm DenMune is able to handle clusters of arbitrary shapes, varying densities, and unbalanced data classes effectively. It is based on the mutual nearest neighbor principle and can stably detect and remove noise while detecting target clusters.