An indiscernibility-based clustering method

This paper presents an indiscernibility-based clustering method that can handle relative proximity. The main advantage of this method is that it can be applied to proximity measures that do not satisfy the triangular inequality. Additionally, it may be used with a proximity matrix - thus, it does not require direct access to the original data values. In the experiments, we demonstrate, with the use of partially mutated proximity matrices, that this method produces good clusters even when the employed proximity does not satisfy the triangular inequality.