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 …

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 K-means algorithm is a popular data-clustering algorithm. However, one of its
drawbacks is the requirement for the number of clusters, K, to be specified before the …

The k-means algorithm is one of the most widely used methods to partition a dataset into
groups of patterns. However, most k-means methods require expensive distance …

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 …

This paper surveys some historical issues related to the well-known k-means algorithm in
cluster analysis. It shows to which authors the different versions of this algorithm can be …

The K-means algorithm for clustering is very much dependent on the initial seed values. We
use a genetic algorithm to find a near-optimal partitioning of the given data set by selecting …

We present the global k-means algorithm which is an incremental approach to clustering
that dynamically adds one cluster center at a time through a deterministic global search …

In k-means clustering, we are given a set of n data points in d-dimensional space R/sup
d/and an integer k and the problem is to determine a set of k points in Rd, called centers, so …