Improved pruning of large data sets for the minimum enclosing ball problem

Minimum enclosing ball algorithms are studied extensively as a tool in approximation and classification of multidimensional data. We present pruning techniques that can accelerate several existing algorithms by continuously removing interior points from the input. By recognizing a key property shared by these algorithms, we derive tighter bounds than have previously been presented, resulting in twice the effect on performance. Furthermore, only minor modifications are required to incorporate the pruning procedure. The presented bounds are independent of the dimension, and empirical evidence shows that the pruning procedure remains effective in dimensions up to at least 200. In some cases, performance improvements of two orders of magnitude are observed for large data sets.