Clustering Genetic Algorithm

Kudová, Petra

doi:10.1109/dexa.2007.4312873

Cited by 3 publications

(7 citation statements)

References 7 publications

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“…In the proposed work two techniques are clubbed together that are (i) Clustering growth (ii) Genetic Programming, for the evolution of digital circuit. The desired combinatorial digital circuit performance [11] is regenerated with [10], using the CGA method of evolution and the results are illustrated in table III and IV. Our work combines the two tools for the synthesis of arithmetic circuits using cell crossover operator and a fast fitness function [3].…”

Section: Resultsmentioning

confidence: 99%

“…K-means [9,10]: It is distance based clustering which can be used to make a given number of clusters from the data.…”

Section: B Clusteringmentioning

confidence: 99%

“…Genetic algorithm [10]: Every chromosome is a cluster and each gene is an object. Gene with same allele value is in same cluster.…”

Section: B Clusteringmentioning

confidence: 99%

“…The notion of similarity is problem dependent. Clustering [9,10] based on genetic algorithm gives a far better results. In this paper clustering using genetic algorithm has been used to find the optimum solutions.…”

Section: Introductionmentioning

confidence: 97%

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Cluster growth technique for combinatorial evolvable digital circuits

Srivastava

Gupta

2014

2014 Seventh International Conference on Contemporary Computing (IC3)

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The genetic algorithm (GA) is one of the optimization techniques of evolutionary algorithm used to design evolvable hardware. This paper proposes the application of cluster based growth approach with genetic algorithm for evolvable hardware. Earlier methods of evolving hardware proposed on static m x n grid structure of hardware that is evolved by reconfiguring interconnections. Clustering technique is a synthesis method where the hardware functionality is evaluated for minimum number of gates. The cluster grows by adding more gates into the cluster if the functionality is not obtained. Reconfiguration of interconnections is also preformed along with the cluster growth. Our main contributions are: 1) Adaptation of genetic operators in a way suitable for clustering growth .2) Applications to task with unknown number of clusters in Clustering with genetic algorithm and demonstrate its performance. 3) To obtain the desired functionality with least number of logic gates such that the interconnection of nodes in the earlier defined architecture is modified with clustering, so that the fast convergence is obtained.

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Section: Resultsmentioning

confidence: 99%

“…K-means [9,10]: It is distance based clustering which can be used to make a given number of clusters from the data.…”

Section: B Clusteringmentioning

confidence: 99%

“…Genetic algorithm [10]: Every chromosome is a cluster and each gene is an object. Gene with same allele value is in same cluster.…”

Section: B Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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Cluster growth technique for combinatorial evolvable digital circuits

Srivastava

Gupta

2014

2014 Seventh International Conference on Contemporary Computing (IC3)

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“…Moreover, the distance between two clusters i and j is d(C i , C j ). The agglomerative hierarchical clustering algorithm was taken from [7], [12], [13], and its steps can be briefly mentioned as shown below.…”

Section: Analysis Of Some Algorithms For Clustering Data Objectsmentioning

confidence: 99%

Analysis of Some Algorithms for Clustering Data Objects

Elsayed¹,

Qasem²

2014

IJMLC

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The main objective of clustering is to partition a set of objects into groups or clusters. The objects within a cluster are more similar to one another than those of the others clusters. This work analyzes, discusses and compares three clustering algorithms. The algorithms are based on partitioning, hierarchical, and swarm intelligence approaches. The three algorithms are k-means clustering, hierarchical agglomerative clustering, and ant clustering respectively. The algorithms are tested using three different datasets. Some measurable criteria are used for evaluating the performance of such algorithms. The criteria are: intra-cluster distance, intercluster distance, and clustering time. The experimental results showed that the k-means algorithm is faster and easily understandable than the other two algorithms. The k-means algorithm is not capable of determining the appropriate number of clusters and depends upon the user to identify this in advance. The ease of handling of any forms of similarity or distance is one of the advantages of the hierarchical clustering algorithm. The disadvantage involves the embedded flexibility regarding the granularity level. The ant-clustering algorithm can detect the more similar data for larger values of swarm coefficients. The performance of the ant clustering algorithm outperforms the other two algorithms. This occurs only for the better choice of the swarm parameters; otherwise the agglomerative hierarchical clustering is the best.

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Second Order Mutual Information based Grey Wolf Optimization for effective storage and de-duplication

Malhotra

Bakal

2018

Sādhanā

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This paper intends to perform de-duplication for enhancing the storage optimization by utilizing the similarity in mutual information. Hence, this paper contributes by proposing a hybrid fingerprint extracting using SH and HC algorithms. Secondly, the data is clustered using the latest technique called as SOMI-GO to extract the metadata. The extracted metadata is stored in metadata server which provides better storage optimization and de-duplication. SOMI-GO is adopted as it provides maximum second-order mutual information based on the similarity index. The proposed SOMI-GO technique is compared with the existing methods such as K-means, K-mode, ED-PSO, ED-GA and ED-GWO in terms of accuracy, TPR, TNR and performance time and the significance of the SOMI-GO method is described.

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Clustering Genetic Algorithm

Cited by 3 publications

References 7 publications

Cluster growth technique for combinatorial evolvable digital circuits

Cluster growth technique for combinatorial evolvable digital circuits

Analysis of Some Algorithms for Clustering Data Objects

Second Order Mutual Information based Grey Wolf Optimization for effective storage and de-duplication

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