9786203929171 - improvements over fuzzy clustering methods for large datasets de k., mrudula; e., keshava reddy; t., hitendra sarma (5 resultados)
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Librería: preigu, Osnabrück, Alemaniapreigu
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Taschenbuch. Condición: 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 | Anbiete…r: preigu.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condición: 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 N…2C 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.
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Librería: moluna, Greven, Alemaniamoluna
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Condición: 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 p…ublished with various reputed p.
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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Taschenbuch. Condición: 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 d…istance 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.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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Taschenbuch. Condición: 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 di…stance 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.
