Minimum cross-entropy approximation for modeling of highly intertwining data sets at subclass levels

We study the problem of how to accurately model the data sets that contain a number of highly intertwining sets in terms of their spatial distributions. Applying the Minimum Cross-Entropy minimization technique, the data sets are placed into a minimum number of subclass clusters according to their high intraclass and low interclass similarities. The method leads to a derivation of the probability density functions for the data sets at the subclass levels. These functions then, in combination, serve as an approximation to the underlying functions that describe the statistical features of each data set.