Mountain clustering on nonuniform grids

We describe an improvement on the mountain method (MM) of clustering originally proposed by Yager and Filev. The new technique employs a data-driven, hierarchical partitioning of the data set to be clustered, using a "p-tree" algorithm. The centroids of data subsets in the terminal nodes of the p-tree are the set of candidate cluster centers to which the iterative candidate cluster center selection process of MM is applied. As the data dimension and/or the number of uniform grid lines used in the original MM increases, our approach requires exponentially fewer cluster centers to be evaluated by the MM selection algorithm. Sample data sets illustrate the performance of this new technique.