Clustering is a fundamental tool of scientific analysis, ubiquitous in
disciplines from biology and chemistry to astronomy and pattern
recognition. We propose a novel clustering algorithm based on the
natural idea that a cluster and its nearest neighbor with higher mass
should be merged into one cluster, unless they both have relatively
large masses and the distance between them is also relatively large. The
find of mass and distance peaks reveals the mergers that don’t conform
to the rule and should be removed. The algorithm is parameter-free and
harnesses this idea to recognize any cluster and find the proper number
of clusters and noise autonomously. Experiments on numerous synthetic
and real-world data sets show the enormous versatility of the proposed
algorithm that remarkably outperforms the best compared algorithm.
Additionally, we also compare it with latest state-of-the-art deep
clustering algorithms on several challenging image data sets. The
proposed algorithm without any deep representation achieves better or
close performance than deep clustering algorithms on image clustering.