A new perspective of modified partition coefficient

• A new way to calculate the modified partition coefficient is presented.
• It is obtained by means of an Euclidean distance.
• Different measures of divergence were alternatively attempted.
• Sensitivity to the initial conditions of fuzzy c-means algorithm was studied.
• A Chernoff’s measure of discriminatory information provides more robust results.

We begin by showing that the modified partition coefficient (MPC) is an average Euclidean distance between membership degrees and the centre of the fuzzy c-partition. Subsequently, we construct alternative MPCs using several other measures of dissimilarity and examine how differently they perform when compared with the original proposal. Empirical evidence shows that the MPC based on a Chernoff’s measure of divergence is more robust to the initial conditions of the fuzzy c-means algorithm.