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Genetic K-means Algorithm

by K. Krishna, M. Narasimha Murty - IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS, PART B: CYBERNETICS , 1999
"... In this paper, we propose a novel hybrid genetic algorithm (GA) that finds a globally optimal partition of a given data into a specified number of clusters. GA’s used earlier in clustering employ either an expensive crossover operator to generate valid child chromosomes from parent chromosomes or a ..."
Abstract - Cited by 93 (0 self) - Add to MetaCart
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

Convergence Properties of the K-Means Algorithms

by Léon Bottou, Yoshua Bengio
"... 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 act ..."
Abstract - Cited by 111 (2 self) - Add to MetaCart
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

A Modified K-means Algorithms- Bi-Level K-Means Algorithm

by Shyr-shen Yu, Shao-wei Chu, Ching-lin Wang, Chia -yi Chuang
"... 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 clus ..."
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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

A Review of K-mean Algorithm

by Jyoti Yadav , Monika Sharma , 2013
"... 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 ..."
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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

Redefining and Enhancing K-means Algorithm

by Nimrat Kaur Sidhu , Rajneet Kaur
"... 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 of cluster ..."
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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

Clustering and the continuous k-means algorithm

by Vance Faber - Los Alamos Science , 1994
"... Many types of data analysis, such as the interpretation of Landsat images discussed in the accompanying article, involve datasets so large that their direct manipulation is impractical. Some method of data compression or consolidation must first be applied to reduce the size of the dataset without l ..."
Abstract - Cited by 59 (0 self) - Add to MetaCart
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

A Fast K-Means Algorithm

by Eena Gilhotra, Priyanka Trikha
"... In clustering, we are given a set of N points in d-‐dimension space R d and we have to arrange them into a number of groups (called clusters). In k-‐means clustering, the groups are identified by a set of points that are called the cluster centers. The data points belong to the cluster whose center ..."
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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

Adaptation of K-Means Algorithm for Image Segmentation

by Ali Salem, Bin Samma, Rosalina Abdul Salam
"... 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 numb ..."
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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

A Prototypes-Embedded Genetic K-means Algorithm

by Shih-sian Cheng, Yi-hsiang Chao, Hsin-min Wang, Hsin-chia Fu
"... 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 al ..."
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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

Parallel K-Means Algorithm on Agricultural Databases

by unknown authors
"... 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 dat ..."
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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
Results 1 - 10 of 5,098