Abstract:
The rapid growth in the volume of processed information that takes
place nowadays determines the urgency of the development of methods
for reducing the dimension of computational problems. One of the
approaches to reducing the dimensionality of data is their
clustering, i.e., uniting into maximally homogeneous groups. At the
same time, it is desirable that representatives of different
clusters should be as much as possible unlike each other. Along with
the dimension reduction, clustering procedures have an independent
value. For example, we know the market segmentation problem in
economics, the feature typologization problem in sociology, faces
diagnostics in geology, etc.
Despite the large number of known clusterization methods, the
development and study of new ones remain relevant. The reason is
that there is no algorithm that would surpass all the rest by all
criteria (speed, insensitivity to clusters' size and shape, number
of input parameters, etc.).
In this paper, we propose a clustering algorithm based on the
notions of the graph theory (namely, the maximum flow (the minimum
cut) theorem) and compare the results obtained by it and by four
other algorithms that belong to various classes of clusterization
techniques.