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Genetic Kmeans Algorithm
 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 ..."
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Cited by 93 (0 self)
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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., Kmeans algorithm. Hence, the name genetic Kmeans algorithm (GKA). We define Kmeans operator, onestep of Kmeans algorithm, and use
Convergence Properties of the KMeans Algorithms
"... This paper studies the convergence properties of the well known KMeans clustering algorithm. The KMeans 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 KMeans algorithm act ..."
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Cited by 111 (2 self)
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This paper studies the convergence properties of the well known KMeans clustering algorithm. The KMeans 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 KMeans algorithm
A Modified Kmeans Algorithms BiLevel KMeans Algorithm
"... Abstract—In this paper, a modified Kmeans algorithm is proposed to categorize a set of data into smaller clusters. Kmeans algorithm is a simple and easy clustering method which can efficiently separate a huge number of continuous numerical data with highdimensions. Moreover, the data in each clus ..."
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Abstract—In this paper, a modified Kmeans algorithm is proposed to categorize a set of data into smaller clusters. Kmeans algorithm is a simple and easy clustering method which can efficiently separate a huge number of continuous numerical data with highdimensions. Moreover, the data in each
A Review of Kmean Algorithm
, 2013
"... AbstractCluster 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. Kmean is the most popular partitional clustering method. In this paper we discuss standard kmean algorithm and analyze the shortcoming ..."
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AbstractCluster 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. Kmean is the most popular partitional clustering method. In this paper we discuss standard kmean algorithm and analyze the shortcoming
Redefining and Enhancing Kmeans Algorithm
"... ABSTRACT: This paper aims at finding the value of number of clusters in advance and to increase the overall performance of Kmeans algorithm. Although there are various methods for removing the disadvantages of kmeans 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 Kmeans algorithm. Although there are various methods for removing the disadvantages of kmeans algorithm as the main problem is how to calculate the value of number
Clustering and the continuous kmeans algorithm
 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 ..."
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Cited by 59 (0 self)
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that consolidate data by clustering, or grouping, and then present a new method, the continuous kmeans algorithm, * developed at the Laboratory specifically for clustering large datasets. Clustering involves dividing a set of data points into nonoverlapping groups, or clusters, of points, where points in a
A Fast KMeans Algorithm
"... 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 KMeans Algorithm for Image Segmentation
"... Abstract — Image segmentation based on an adaptive Kmeans clustering algorithm is presented. The proposed method tries to develop Kmeans algorithm to obtain high performance and efficiency. This method proposes initialization step in Kmeans algorithm. In addition, it solves a model selection numb ..."
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Cited by 4 (0 self)
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Abstract — Image segmentation based on an adaptive Kmeans clustering algorithm is presented. The proposed method tries to develop Kmeans algorithm to obtain high performance and efficiency. This method proposes initialization step in Kmeans algorithm. In addition, it solves a model selection
A PrototypesEmbedded Genetic Kmeans Algorithm
"... This paper presents a genetic algorithm (GA) for Kmeans clustering. Instead of the widely applied stringofgroupnumbers encoding, we encode the prototypes of the clusters into the chromosomes. The crossover operator is designed to exchange prototypes between two chromosomes. The onestep Kmeans al ..."
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This paper presents a genetic algorithm (GA) for Kmeans clustering. Instead of the widely applied stringofgroupnumbers encoding, we encode the prototypes of the clusters into the chromosomes. The crossover operator is designed to exchange prototypes between two chromosomes. The onestep Kmeans
Parallel KMeans Algorithm on Agricultural Databases
"... A cluster is a collection of data objects that are similar to each other and dissimilar to the data objects in other clusters. Kmeans 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. Kmeans 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
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5,098