An efficient clustering algorithm from the measure of local Gaussian distribution
arXiv:1709.08470v2
Originality Synthesis-oriented
AI Analysis
This work addresses clustering efficiency for data analysis, but appears incremental as it builds on Gaussian distribution methods.
The paper tackles the problem of clustering by introducing a fast algorithm that ensures cluster centroids are separated by a given parameter, with a worst-case runtime complexity of approximately O(T × N × log(N)).
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.