Dealing with relatively proximity by rough clustering

This paper presents a new clustering method based on the indiscernibility of objects. It provides good partition to objects even when the proximity of objects is defined as relative proximity. The main benefit 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.