… algorithm used in clustering viz., K-means algorithm. Hence, the name genetic K-means
algorithm (GKA). We define K-means operator, one-step of K-means algorithm, and use it in …

… on the k-means algorithm to overcome such shortcomings. Variants of the k-means algorithms
… a thorough comparison among different k-means clustering algorithms differentiates our …

… -means that discovers an appropriate k using a statistical test for deciding whether to split a
k-means … other than k-means, but here we consider only the k-means algorithm for simplicity. …

… k-means algorithm is not exactly an unsupervised clustering method. In this paper, we
construct an unsupervised learning schema for the k-means … unsupervised k-means (Ukmeans) …

… The second part details the kmeans algorithm and shows the shortcomings of the standard
kmeans algorithm. The third part presents the improved k-means clustering algorithm, the last …

… K-mean is the most popular partitional clustering method. In this paper … k-mean algorithm
and analyze the shortcoming of kmean algorithm. In this paper three dissimilar modified k-mean …

… k-means algorithm, to which we refer as “k-MEANSMTD” throughout this paper, performs the
k-MEANS … misclassified points and consequently in the last k-MEANS step S = S. Clearly the …

… More recently, we have found that using K-means clustering as the unsupervised learning
… K-means has already been identified as a successful method to learn features from images …

This paper synthesizes the results, methodology, and research conducted concerning the K‐means
clustering method over the last fifty years. The K‐means method is first introduced, …

… mean squared distance from each data point to its nearest center. A popular heuristic for k-means …
In this paper we present a simple and efficient implementation of Lloyd's k-means …