Items related to Rough Fuzzy Clustering Using Decision Theory: A Data...
Synopsis
Clustering is the unsupervised classification of patterns into groups. In non-fuzzy or hard clustering, data is divided into crisp clusters, where each data point belongs to exactly one cluster. In fuzzy clustering, the data points can belong to more than one cluster, and associated with each of the points are membership grades which indicate the degree to which the data points belong to the different clusters. Different clustering algorithms produce clusters with different characteristics. In this Book specifies the Clustering techniques using decision theory.
"synopsis" may belong to another edition of this title.
About the Author
Ammisetty Veeraswamy is a Ph.D. Candidate at VELTECH Dr.RR & Dr.SR TECHNICAL UNIVERSITY, and Chennai, India. He is working as an Asst.Professor, Department of Computer Science Engineering at VRS & YRN College of Engineering & Technology, Chirala,A.P, India. He was Published Several Papers in International Journals & Conferences.
"About this title" may belong to another edition of this title.
- PublisherLAP LAMBERT Academic Publishing
- Publication date2012
- ISBN 10 3659297208
- ISBN 13 9783659297205
- BindingPaperback
- LanguageEnglish
- Number of pages100
Buy New
View this item
US$ 54.52
Shipping:
US$ 24.85
From Germany to U.S.A.
Search results for Rough Fuzzy Clustering Using Decision Theory: A Data...
Seller Image
Rough Fuzzy Clustering Using Decision Theory
Published by
LAP LAMBERT Academic Publishing Nov 2012, 2012
ISBN 10: 3659297208
ISBN 13: 9783659297205
New
Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Clustering is the unsupervised classification of patterns into groups. In non-fuzzy or hard clustering, data is divided into crisp clusters, where each data point belongs to exactly one cluster. In fuzzy clustering, the data points can belong to more than one cluster, and associated with each of the points are membership grades which indicate the degree to which the data points belong to the different clusters. Different clustering algorithms produce clusters with different characteristics. In this Book specifies the Clustering techniques using decision theory. 100 pp. Englisch. Seller Inventory # 9783659297205
Quantity: 2 available
Seller Image
Rough Fuzzy Clustering Using Decision Theory : A Data mining Approach
Published by
LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3659297208
ISBN 13: 9783659297205
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Clustering is the unsupervised classification of patterns into groups. In non-fuzzy or hard clustering, data is divided into crisp clusters, where each data point belongs to exactly one cluster. In fuzzy clustering, the data points can belong to more than one cluster, and associated with each of the points are membership grades which indicate the degree to which the data points belong to the different clusters. Different clustering algorithms produce clusters with different characteristics. In this Book specifies the Clustering techniques using decision theory. Seller Inventory # 9783659297205
Quantity: 1 available
Seller Image
Rough Fuzzy Clustering Using Decision Theory
Published by
LAP LAMBERT Academic Publishing, 2012
ISBN 10: 3659297208
ISBN 13: 9783659297205
New
Kartoniert / Broschiert
Seller: moluna, Greven, Germany
Seller rating 5 out of 5 stars
Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Veeraswamy AmmisettyAmmisetty Veeraswamy is a Ph.D. Candidate at VELTECH Dr.RR & Dr.SR TECHNICAL UNIVERSITY, and Chennai, India. He is working as an Asst.Professor, Department of Computer Science Engineering at VRS & YRN College of E. Seller Inventory # 5146595
Quantity: Over 20 available