DenMune: Density peak based clustering using mutual nearest neighbors
- URL: http://arxiv.org/abs/2309.13420v1
- Date: Sat, 23 Sep 2023 16:18:00 GMT
- Title: DenMune: Density peak based clustering using mutual nearest neighbors
- Authors: Mohamed Abbas, Adel El-Zoghobi, Amin Shoukry
- Abstract summary: Many clustering algorithms fail when clusters are of arbitrary shapes, of varying densities, or the data classes are unbalanced and close to each other.
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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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