  Tree rotations (left and right) are basic local deformations allowing to
transform between two unlabeled binary trees of the same size. Hence, there is
a natural problem of practically finding such transformation path with low
number of rotations, the optimal minimal number is called the rotation
distance. Such distance could be used for instance to quantify similarity
between two trees for various machine learning problems, for example to compare
hierarchical clusterings or arbitrarily chosen spanning trees of two graphs,
like in SMILES notation popular for describing chemical molecules.
  There will be presented inexpensive practical greedy algorithm for finding a
short rotation path, optimality of which has still to be determined. It uses
introduced partial order for binary trees of the same size: $t_1 \leq t_2$ iff
$t_2$ can be obtained from $t_1$ by a sequence of only right rotations.
Intuitively, the shortest rotation path should go through the least upper bound
or the greatest lower bound for this partial order. The algorithm finds a path
through candidates for both points in representation of binary tree as stack
graph: describing evolution of content of stack while processing a formula
described by a given binary tree. The article is accompanied with Mathematica
implementation of all used procedures (Appendix).
