The self-organizing map of attribute trees

The standard version of the self-organizing map (Kohonen (1990)) applies vector data. This paper explains how attribute trees can be used as the learning medium in the self-organizing map. As a data structure, a tree is an optimal presentation of many hierarchical and dynamical objects appearing in natural phenomena and human activities. The proposed approach is based on introducing a distance metric and adjusting schemes for attribute trees. The trees are assumed to be rooted and unordered. The key idea is in heuristic matching which provides approximate results but above all avoids the exponential complexity of exact matching. The feasibility of the suggested methods is demonstrated with an experiment on weather radar imagery