Distance in Fuzzy Graphs | g-distance, µ -distance, - distance, and ss-distance. Este artículo no está disponible.
Idioma: inglés
Editorial: LAP Lambert Academic Publishing, 2012
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- Nuevo
Librería: preigu, Osnabrück, Alemaniapreigu
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Distance in Fuzzy Graphs | g-distance, µ -distance, - distance, and ss-distance | Sameena Kalathodi (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2012 | LAP Lambert Academic Publishing | EAN 9783659169342 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu.
N° de ref. del artículo 106376114
- Título
- Distance in Fuzzy Graphs | g-distance, µ -distance, - distance, and ss-distance
- Autor
- Sameena Kalathodi (u. a.)
- Editorial
- LAP Lambert Academic Publishing
- Año de publicación
- 2012
- Estado
- Neu
- Encuadernación
- Taschenbuch
- Idioma
- inglés
- ISBN 10
- 365916934X
- ISBN 13
- 9783659169342
- Peso del artículo
- 182 gramos
- Dimensiones
- 8 x 150 x 220 mm
- Catálogos de vendedores
- Bücher
This work mainly focuses on different distance concepts in fuzzy graphs. The properties of δ- distance,µ -distance and g-distance are studied and defined ss-distance in a connected fuzzy graph. A study of g-distance in a fuzzy tree and its associated maximum spanning tree is carried out.Various types of degrees of a node and its properties in fuzzy graphs are studied. The concept of strong cycle is introduced and studied the properties of fuzzy end nodes and strong cycles in a fuzzy graph. Clustering techniques using distance concepts in fuzzy graphs are discussed and introduced a procedure for finding clusters of order k using distance matrix. Also clustering techniques based on the connectedness concepts in fuzzy graphs are discussed. Fuzzy graph theoretic techniques are applied in fuzzy neural network.
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Reseña del editor
This work mainly focuses on different distance concepts in fuzzy graphs. The properties of δ- distance,µ -distance and g-distance are studied and defined ss-distance in a connected fuzzy graph. A study of g-distance in a fuzzy tree and its associated maximum spanning tree is carried out.Various types of degrees of a node and its properties in fuzzy graphs are studied. The concept of strong cycle is introduced and studied the properties of fuzzy end nodes and strong cycles in a fuzzy graph. Clustering techniques using distance concepts in fuzzy graphs are discussed and introduced a procedure for finding clusters of order k using distance matrix. Also clustering techniques based on the connectedness concepts in fuzzy graphs are discussed. Fuzzy graph theoretic techniques are applied in fuzzy neural network.
“Acerca de” puede pertenecer a otra edición de este título.
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