K-means seeding via MUS algorithm

K-means algorithm is one of the most popular procedures in data clustering. Despite its large use, one major criticism is the impact of the initial seeding on the final solution. We propose a modification of the K-means algorithm, based on a suitable choice of the initial centers. Similarly to clustering ensemble methods, our approach takes advantage of the information contained in a co-association matrix. Such matrix is given as input for the MUS algorithm that allows to define a pivot-based initialization step. Preliminary results concerning the comparison with the classical approach are discussed.