The k-means method aims to divide a set of N objects into k clusters, where each cluster is
represented by the mean value of its objects. This algorithm is simple and converges to local …

Mining knowledge from large amounts of spatial data is known as spatial data mining. It
becomes a highly demanding field because huge amounts of spatial data have been …

The k-means tries to minimize the sum of the squared Euclidean distance from the mean
(SSEDM) of each cluster as its objective function. Although this algorithm is effective, it is too …

The-means algorithm is one of the most popular clustering methods. However, it is a well-
known fact that its performance, in terms of quality of the obtained solution and …

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 …

The k-means algorithm is one of the most widely used clustering heuristics. Despite its
simplicity, analyzing its running time and quality of approximation is surprisingly difficult and …

Clustering is the process of grouping the data based on their similar properties. Meanwhile,
it is the categorization of a set of data into similar groups (clusters), and the elements in each …

This paper defines nearest neighbor pair and puts forward four assumptions about nearest
neighbor pairs, based on which a center initialization method for K-means algorithm over …

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 …

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