Stochastic gradient descent possibilistic clustering
Aggeliki Koutsibella, Konstantinos Koutroumbas · 2020
Although online versions of several well known clustering algorithms have been proposed, in order to deal effectively with the big data issue, as well as with the case where the data are available in a streaming fashion, very few of them follow the stochastic gradient descent philosophy. In this paper a novel stochastic gradient descent possibilistic clustering algorithm, called O-PCM2 is introduced. The algorithm is presented in detail and a convergence proof of it is provided, based on the general convergence results established for the family of the stochastic gradient descent algorithms. Finally, the effectiveness of the proposed algorithm is assessed through extensive experimentation on both simulated and real data sets and in comparison with other related algorithms.