  A novel nonparametric clustering algorithm is proposed using the interpoint
distances between the members of the data to reveal the inherent clustering
structure existing in the given set of data, where we apply the classical
nonparametric univariate kernel density estimation method to the interpoint
distances to estimate the density around a data member. Our clustering
algorithm is simple in its formation and easy to apply resulting in
well-defined clusters. The algorithm starts with objective selection of the
initial cluster representative and always converges independently of this
choice. The method finds the number of clusters itself and can be used
irrespective of the nature of underlying data by using an appropriate
interpoint distance measure. The cluster analysis can be carried out in any
dimensional space with viability to high-dimensional use. The distributions of
the data or their interpoint distances are not required to be known due to the
design of our procedure, except the assumption that the interpoint distances
possess a density function. Data study shows its effectiveness and superiority
over the widely used clustering algorithms.
