We propose a tool for exploring the number of clusters based on pivotal methods and consensus clustering. K-means algorithm is used to learn the pairwise similarity via the co-occurrence of points in multiple partitions of the data. This similarity can be used to investigate the number of groups and detect arbitrary shaped clusters. Different criteria for identifying the pivots are discussed, as well as preliminary results concerning the selection of the optimal number of clusters.
