Synopsis
Fuzzy C-means and kernel FCM-F are multi scan methods and require NC distance computations where N is the size of the dataset D, C is the number of cluster centers in the data and Kernel FCM-K also is a multi scan method requiring N2C distance computations in each iteration. For large values of N, the overall computation cost will go on increasing for these methods. The Book proposed two-step prototype based hybrid techniques to speed-up FCM, KFCM-F and KFCM-K. The proposed algorithms are called Prototype based FCM (PFCM), Prototype based KFCM- F (PKFCM-F) and Prototype based KFCM-K(PKFCM-K). Initially, few prototypes are generated from the given dataset and later the conventional methods are applied on these selected prototypes. The present work focuses on reducing the time complexities of these methods without effecting the Clustering Accuracy. The reduction in running time will make these methods work efficiently on very large data sets.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Résultats de recherche pour Improvements over Fuzzy clustering methods for large...
Image fournie par le vendeur
Improvements over Fuzzy clustering methods for large Datasets
Edité par
LAP LAMBERT Academic Publishing Jul 2021, 2021
ISBN 10 : 6203929174
ISBN 13 : 9786203929171
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Fuzzy C-means and kernel FCM-F are multi scan methods and require NC distance computations where N is the size of the dataset D, C is the number of cluster centers in the data and Kernel FCM-K also is a multi scan method requiring N2C distance computations in each iteration. For large values of N, the overall computation cost will go on increasing for these methods. The Book proposed two-step prototype based hybrid techniques to speed-up FCM, KFCM-F and KFCM-K. The proposed algorithms are called Prototype based FCM (PFCM), Prototype based KFCM- F (PKFCM-F) and Prototype based KFCM-K(PKFCM-K). Initially, few prototypes are generated from the given dataset and later the conventional methods are applied on these selected prototypes. The present work focuses on reducing the time complexities of these methods without effecting the Clustering Accuracy. The reduction in running time will make these methods work efficiently on very large data sets. 56 pp. Englisch. N° de réf. du vendeur 9786203929171
Acheter neuf
EUR 39,90
Expédition à EUR 23
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 2 disponible(s)
Image fournie par le vendeur
Improvements over Fuzzy clustering methods for large Datasets
Edité par
LAP LAMBERT Academic Publishing, 2021
ISBN 10 : 6203929174
ISBN 13 : 9786203929171
Neuf
Couverture souple
Vendeur : moluna, Greven, Allemagne
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: K. MrudulaK. Mrudula obtained her Ph.D in Mathematics from Jawaharlal Nehru Technological University in 2019. She has more than 10 years of Teaching & Research experience. Her research works have been published with various reputed p. N° de réf. du vendeur 493007015
Acheter neuf
EUR 34,25
Expédition à EUR 48,99
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Improvements over Fuzzy clustering methods for large Datasets
Edité par
LAP LAMBERT Academic Publishing Jul 2021, 2021
ISBN 10 : 6203929174
ISBN 13 : 9786203929171
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Fuzzy C-means and kernel FCM-F are multi scan methods and require NC distance computations where N is the size of the dataset D, C is the number of cluster centers in the data and Kernel FCM-K also is a multi scan method requiring N2C distance computations in each iteration. For large values of N, the overall computation cost will go on increasing for these methods. The Book proposed two-step prototype based hybrid techniques to speed-up FCM, KFCM-F and KFCM-K. The proposed algorithms are called Prototype based FCM (PFCM), Prototype based KFCM- F (PKFCM-F) and Prototype based KFCM-K(PKFCM-K). Initially, few prototypes are generated from the given dataset and later the conventional methods are applied on these selected prototypes. The present work focuses on reducing the time complexities of these methods without effecting the Clustering Accuracy. The reduction in running time will make these methods work efficiently on very large data sets.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 56 pp. Englisch. N° de réf. du vendeur 9786203929171
Acheter neuf
EUR 39,90
Expédition à EUR 60
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 1 disponible(s)
Image fournie par le vendeur
Improvements over Fuzzy clustering methods for large Datasets
Edité par
LAP LAMBERT Academic Publishing, 2021
ISBN 10 : 6203929174
ISBN 13 : 9786203929171
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Fuzzy C-means and kernel FCM-F are multi scan methods and require NC distance computations where N is the size of the dataset D, C is the number of cluster centers in the data and Kernel FCM-K also is a multi scan method requiring N2C distance computations in each iteration. For large values of N, the overall computation cost will go on increasing for these methods. The Book proposed two-step prototype based hybrid techniques to speed-up FCM, KFCM-F and KFCM-K. The proposed algorithms are called Prototype based FCM (PFCM), Prototype based KFCM- F (PKFCM-F) and Prototype based KFCM-K(PKFCM-K). Initially, few prototypes are generated from the given dataset and later the conventional methods are applied on these selected prototypes. The present work focuses on reducing the time complexities of these methods without effecting the Clustering Accuracy. The reduction in running time will make these methods work efficiently on very large data sets. N° de réf. du vendeur 9786203929171
Acheter neuf
EUR 40,89
Expédition à EUR 60,51
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 1 disponible(s)
Image fournie par le vendeur
Improvements over Fuzzy clustering methods for large Datasets
Edité par
LAP LAMBERT Academic Publishing, 2021
ISBN 10 : 6203929174
ISBN 13 : 9786203929171
Neuf
Taschenbuch
Vendeur : preigu, Osnabrück, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Improvements over Fuzzy clustering methods for large Datasets | Mrudula K. (u. a.) | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786203929171 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 120423289
Acheter neuf
EUR 36,25
Expédition à EUR 70
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 5 disponible(s)