Automatic selection of the number of clusters in multidimensional data problems

When processing multidimensional remote sensing data, one of the main problem is the choice for the appropriate number of clusters; despite of the great number of good algorithms for clustering, each of them works properly only when the appropriate number of clusters is selected. As adaptive versions of the K-means, competitive learning (CL) algorithms also have a similar crucial problem; various efforts to improve the performance of CL were made with the introduction of frequency sensitive competitive learning (FSCL) and rival penalised competitive learning (RPCL). We present an improvement of the RPCL algorithm well adapted to work with every kind of real clustering data problems. The basic idea of this new algorithm is to introduce a competition also between the weights. The algorithm was tested on multiband images with different weights initial position, giving similar results.