K-means (MacQueen, 1967) [1] is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. The procedure follows a simple and easy way to classify a given data set to a predefined, say K number of clusters. Determination of K is a difficult job and it is not known that which value of K can partition the objects as per our intuition. To overcome this probl...

This paper presents a generalized version of the conventional k-means clustering algorithm [Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, University of California Press, Berkeley, 1967, p. 281]. Not only is this new one applicable to ellipse-shaped data clusters without dead-unit problem, but also performs correct clustering without pre-assigning the exact...

a well-known algorithm of clustering is k-means by which the data are divided into k classes based upon a distance criterion. in the present research, by using k-means method for classifying data derived from exploration boreholes in the parkam deposit, the optimum k has been calculated and then the data have been clustered and the relative geochemical behavioral characteristics analyzed. the c...

k-means++ [5] seeding procedure is a simple sampling based algorithm that is used to quickly find k centers which may then be used to start the Lloyd’s method. There has been some progress recently on understanding this sampling algorithm. Ostrovsky et al. [10] showed that if the data satisfies the separation condition that ∆k−1(P ) ∆k(P ) ≥ c (∆i(P ) is the optimal cost w.r.t. i centers, c > 1...

Background: Microarray technology has revolutionized the way genomic analysis has been performed. High-throughput data acquisition, brought up a challenge in data comprehension i.e. in gene expression. Methods: k-means cluster obtained after analysis of miRNA expression data have been sorted by an algorithmic procedure. Results: The proposed method managed to sort k-means centroids and manifest...

The Lloyd’s algorithm, also known as the k-means algorithm, is one of the most popular algorithms for solving the k-means clustering problem in practice. However, it does not give any performance guarantees. This means that there are datasets on which this algorithm can behave very badly. One reason for poor performance on certain datasets is bad initialization. The following simple sampling ba...