An approach is presented that automatically discovers different cluster shapes that are hard to discover by traditional
clustering methods (e.g., non-spherical shapes). This allows discover useful knowledge by dividing the datasets into sub
clusters; in which each one have similar objects. The approach does not compute the distance between objects but
instead the similarity information between objects is computed as needed while using the topological relations as a new
similarity measure. An efficient tool was developed to support the approach and is applied to a multiple synthetic and
real datasets. The results are evaluated and compared against different clustering methods using different comparison
measures such as accuracy, number of parameters, time complexity, and visually inspection. The tool performs better
than error-prone distance clustering methods in both the time complexity and the accuracy of the results.