An efficient clustering algorithm from the measure of local Gaussian distribution

TL;DR

This paper introduces a fast clustering algorithm leveraging Gaussian distribution that guarantees cluster separation with a specified parameter, achieving near-linear runtime complexity.

Contribution

The paper presents a novel clustering method based on local Gaussian measures with guaranteed cluster separation and efficient computational complexity.

Findings

Algorithm guarantees cluster separation based on parameter $d_s$

Achieves runtime complexity approximately $O(T imes N imes ext{log}(N))$

Provides a fast alternative to existing clustering methods

Abstract

In this paper, I will introduce a fast and novel clustering algorithm based on Gaussian distribution and it can guarantee the separation of each cluster centroid as a given parameter, $d_s$. The worst run time complexity of this algorithm is approximately $\sim$O$(T\times N \times \log(N))$ where $T$ is the iteration steps and $N$ is the number of features.