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A clustering algorithm called LA is described. The LA algorithm can be applied to the data represented as a set of values of attributes. The algorithm is based on comparison of the n-dimensional density of the data points in various regions of the space of attributes p(x1,...,xn) with an expected homogeneous density obtained as a simple product of the corresponding one- dimensional densities pi(xi). The regions with a high value of the ratio px x

LA – A Clustering Algorithm with an Automated Selection of Attributes, Which is Invariant to Functional Transformations of Coordinates