The k-means algorithm is known to have a time complexity of O (n 2), where n is the input
data size. This quadratic complexity debars the algorithm from being effectively used in large …

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 …

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 …

This paper introduces an optimized version of the standard K-Means algorithm. The
optimization refers to the running time and it comes from the observation that after a certain …

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 an efficient algorithm, called pattern reduction (PR), for reducing the
computation time of k-means and k-means-based clustering algorithms. The proposed …

This paper presents a data clustering approach using modified K-Means algorithm based on
the improvement of the sensitivity of initial center (seed point) of clusters. This algorithm …

The k-means algorithm is one of the most widely used clustering algorithms and has been
applied in many fields of science and technology. One of the major problems of the k-means …

The process of partitioning a large set of patterns into disjoint and homogeneous clusters is
fundamental in knowledge acquisition. It is called Clustering in the literature and it is applied …

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 …