Abstract
We advise a novel measure to determine the unknown number of clusters underlying a designated sample through implementation of the parametric logistic regression model. The regression analysis is carried out to estimate the probabilities of inclusion for every individual member from data, irrespective of its parent distribution, to each of the clusters under existence. The proposed one is shown to be superior to its well-known rivals by means of both synthetic and real-world data sets, while designed to significantly reduce the computational burden serving our desired purpose.