  We present an algorithm of clustering of many-dimensional objects, where only
the distances between objects are used. Centers of classes are found with the
aid of neuron-like procedure with lateral inhibition. The result of clustering
does not depend on starting conditions. Our algorithm makes it possible to give
an idea about classes that really exist in the empirical data. The results of
computer simulations are presented.
