We develop a simple and effective approach for approximate estimation of the cluster
centers on the basis of the concept of a mountain function. We call the procedure the …

One of the most widely used clustering techniques is the k-means algorithms. Solutions
obtained from this technique are dependent on the initialisation of cluster centres. In this …

METHOD The algorithm requires as input a matrix of M points in N dimensions and a matrix
of K initial cluster centres in N dimensions. The number of points in cluster L is denoted by …

This paper presents a generalized version of the conventional k-means clustering algorithm
[Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, 1 …

The mountain method of clustering and its relative, the subtractive clustering method, are
studied here. A scheme to improve the accuracy of the prototypes obtained by the mountain …

The problem of clustering multivariate observations is viewed as the replacement of a set of
vectors with a set of labels and representative vectors. A general criterion for clustering is …

METHOD The algorithm requires as input a matrix of M points in N dimensions and a matrix
of K initial cluster centres in N dimensions. The number of points in cluster L is denoted by …

Performance of iterative clustering algorithms which converges to numerous local minima
depend highly on initial cluster centers. Generally initial cluster centers are selected …

A measure is presented which indicates the similarity of clusters which are assumed to have
a data density which is a decreasing function of distance from a vector characteristic of the …

This paper describes four recent papers on clustering, each of which approaches the
clustering problem from a different perspective and with different goals. It analyzes the …