Statistics and Its Interface
Volume 9 (2016)
Convergence and stability analysis of mean-shift algorithm on large data sets
Pages: 159 – 170
We present theoretical convergent analysis of mean-shift type of clustering methods for large data sets. It is proved that correct convergence for unsupervised mean shift type of algorithms relies on its ability to successfully transform data points to be clustered into data patterns of a multivariate normal distribution. Our analytical stability analysis suggests that a judiciously chosen supervision mechanism might be essential for correct convergence in dynamical clustering. The proposed theoretical framework could be used to study other dynamical clustering methods.
anti-diffusion, convergence, conservation law, dynamic clustering, entropy, partial differential equations