or a costly fitness function or both. To circumvent these expensive operations, we hybridize GA with a classical gradient descent 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

This paper studies the convergence properties of the well known K-Means clustering algorithm. The K-Means algorithm can be described either as a gradient descent algorithm or by slightly extending the mathematics of the EM algorithm to this hard threshold case. We show that the K-Means algorithm

Abstract—In this paper, a modified K-means algorithm is proposed to categorize a set of data into smaller clusters. K-means algorithm is a simple and easy clustering method which can efficiently separate a huge number of continuous numerical data with high-dimensions. Moreover, the data in each

Abstract-Cluster analysis is a descriptive task that seek to identify homogenous group of object and it is also one of the main analytical method in data mining. K-mean is the most popular partitional clustering method. In this paper we discuss standard k-mean algorithm and analyze the shortcoming

ABSTRACT: This paper aims at finding the value of number of clusters in advance and to increase the overall performance of K-means algorithm. Although there are various methods for removing the disadvantages of k-means algorithm as the main problem is how to calculate the value of number

that consolidate data by clustering, or grouping, and then present a new method, the continuous k-means algorithm, * developed at the Laboratory specifically for clustering large datasets. Clustering involves dividing a set of data points into non-overlapping groups, or clusters, of points, where points in a

is closest. Existing algorithms for k-‐means clustering suffer from two main drawbacks, (i) The algorithms are slow and do not scale to large number of data points and (ii) they converge to different local minima based on the initializations. We present a fast greedy k-‐means algorithm that attacks both

Abstract — Image segmentation based on an adaptive K-means clustering algorithm is presented. The proposed method tries to develop K-means algorithm to obtain high performance and efficiency. This method proposes initialization step in K-means algorithm. In addition, it solves a model selection

This paper presents a genetic algorithm (GA) for Kmeans clustering. Instead of the widely applied stringof-group-numbers encoding, we encode the prototypes of the clusters into the chromosomes. The crossover operator is designed to exchange prototypes between two chromosomes. The one-step K-means

A cluster is a collection of data objects that are similar to each other and dissimilar to the data objects in other clusters. K-means algorithm has been used in many clustering work because of the ease of the algorithm. But time complexity of algorithm remains expensive when it applied on large