This paper introduces a new method for clustering data using a dynamic scheme. An appropriate partitioning is obtained based on both a dissimilarity measure between pairs of entities as well as a dynamic procedure of splitting. A dissimilarity function is defined by using the cost of the optimum path from a datum to each entity on a graph, with the cost of a path being defined as the greatest distance between two successive vertices on the path. The procedure of clustering is dynamic in the sense that the initial problem of determining a partition into an unknown number of natural groupings has been reduced to a sequence of only two class splitting stages. Having arisen from any particular application, the proposed approach could be effective for many domains, and it is especially successful to identify clusters if there is lack of prior knowledge about the data set. The usefulness of the dynamic algorithm to deal with elongated or non-piecewise linear separable clusters as;well as sparse and dense groupings is demonstrated with several data sets.
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