graph clustering using restricted de kumar mohit (5 resultados)

Taschenbuch. Condición: Neu. Neuware -Restricted Neighborhood Search Algorithm(R.N.S.C)is a cost-based clustering technique for clustering the graph into separate clusters, where each cluster has some similar properties.Based on a practitioner's insights, the book focuses on the basic principles and dwells on the complex details of graph clustering using Restricted Neighbourhood Search. The book covers the following: Introduction Experimental results Test Data Random graph Color benchmark graph Parameter training and statistical results Cluster limit Diversification Tabu length Search length Graph coloringBooks on Demand GmbH, Überseering 33, 22297 Hamburg 80 pp. Englisch.

paperback. Condición: New. New. book.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Restricted Neighborhood Search Algorithm(R.N.S.C)is a cost-based clustering technique for clustering the graph into separate clusters, where each cluster has some similar properties.Based on a practitioner's insights, the book focuses on the basic principles and dwells on the complex details of graph clustering using Restricted Neighbourhood Search. The book covers the following: Introduction Experimental results Test Data Random graph Color benchmark graph Parameter training and statistical results Cluster limit Diversification Tabu length Search length Graph coloring 80 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kumar MohitHe is currently Assistant Professor,Department of Computer Science at Invertis University, Bareilly.He has done his post-graduation from Amity University and Research Thesis from Indian Institute of Technology, Banaras Hi.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Restricted Neighborhood Search Algorithm(R.N.S.C)is a cost-based clustering technique for clustering the graph into separate clusters, where each cluster has some similar properties.Based on a practitioner's insights, the book focuses on the basic principles and dwells on the complex details of graph clustering using Restricted Neighbourhood Search. The book covers the following: Introduction Experimental results Test Data Random graph Color benchmark graph Parameter training and statistical results Cluster limit Diversification Tabu length Search length Graph coloring.