An efficient clustering algorithm from the measure of local Gaussian distribution

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.