ABSTRACT K-means clustering algorithm is one of the best known algorithms used in
clustering; nevertheless it has many disadvantages as it may converge to a local optimum …

The famous K-means clustering algorithm is sensitive to the selection of the initial centroids
and may converge to a local minimum of the criterion function value. A new algorithm for …

This paper presents a new approach to overcome one of the most known disadvantages of
the well-known Kmeans clustering algorithm. The problems of classical Kmeans are such as …

The K-means method is a well-known clustering algorithm with an extensive range of
applications, such as biological classification, disease analysis, data mining, and image …

The main problems of K-means algorithm which is a basic algorithm in clustering are
outlined in this paper, such as determination of the optimal clusters, selection of initial …

Abstract {K-MEANS is one of the most popular clustering algorithms. The CPU time required
by K-MEANS is often unacceptable, particularly for large problems. In this article, some new …

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 …

Data clustering is a basic technique to show the structure of a data set. K-means clustering is
a widely acceptable method of data clustering, which follow a partitioned approach for …

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 …

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 …