An incremental nonsmooth optimization algorithm for clustering using L_1 and L_\infty norms
Burak Ordin, Adil Bagirov, Ehsan Mohebi · Journal of Industrial and Management Optimization · 2020
An algorithm is developed for solving clustering problems with the similarity measure defined using the \begin{document}$ L_1 $\end{document} and \begin{document}$ L_\infty $\end{document} norms. It is based on an incremental approach and applies nonsmooth optimization methods to find cluster centers. Computational results on 12 data sets are reported and the proposed algorithm is compared with the \begin{document}$ X $\end{document} -means algorithm.