  Finding the diameter of a dataset in multidimensional Euclidean space is a
well-established problem, with well-known algorithms. However, most of the
algorithms found in the literature do not scale well with large values of data
dimension, so the time complexity grows exponentially in most cases, which
makes these algorithms impractical. Therefore, we implemented 4 simple greedy
algorithms to be used for approximating the diameter of a multidimensional
dataset; these are based on minimum/maximum l2 norms, hill climbing search,
Tabu search and Beam search approaches, respectively. The time complexity of
the implemented algorithms is near-linear, as they scale near-linearly with
data size and its dimensions. The results of the experiments (conducted on
different machine learning data sets) prove the efficiency of the implemented
algorithms and can therefore be recommended for finding the diameter to be used
by different machine learning applications when needed.
