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ISBN 10 : 0521617936 ISBN 13 : 9780521617932
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Edité par Cambridge University Press, 2006
ISBN 10 : 0521617936 ISBN 13 : 9780521617932
Langue: anglais
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Ajouter au panierEtat : New. pp. 222.
Edité par Cambridge University Press, 2006
ISBN 10 : 0521617936 ISBN 13 : 9780521617932
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Ajouter au panierEtat : New. pp. 222.
Edité par Cambridge University Press, 2006
ISBN 10 : 0521617936 ISBN 13 : 9780521617932
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Edité par Cambridge University Press, 2006
ISBN 10 : 0521617936 ISBN 13 : 9780521617932
Langue: anglais
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Ajouter au panierEtat : Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
Edité par Cambridge University Press 2007-02-22, 2007
ISBN 10 : 0521617936 ISBN 13 : 9780521617932
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Ajouter au panierPaperback. Etat : New.
Edité par Cambridge University Press, 2006
ISBN 10 : 0521617936 ISBN 13 : 9780521617932
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Edité par Cambridge University Press, 2006
ISBN 10 : 0521617936 ISBN 13 : 9780521617932
Langue: anglais
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Edité par Cambridge University Press 11/13/2006, 2006
ISBN 10 : 0521617936 ISBN 13 : 9780521617932
Langue: anglais
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Ajouter au panierPaperback or Softback. Etat : New. Introduction to Clustering Large and High-Dimensional Data 0.65. Book.
Edité par Cambridge University Press, 2006
ISBN 10 : 0521617936 ISBN 13 : 9780521617932
Langue: anglais
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Edité par Cambridge University Press, 2006
ISBN 10 : 0521617936 ISBN 13 : 9780521617932
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Edité par Cambridge University Press, 2006
ISBN 10 : 0521617936 ISBN 13 : 9780521617932
Langue: anglais
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Ajouter au panierEtat : As New. Unread book in perfect condition.
Edité par Cambridge University Press, 2006
ISBN 10 : 0521617936 ISBN 13 : 9780521617932
Langue: anglais
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Ajouter au panierPaperback. Etat : Brand New. 1st edition. 205 pages. 8.75x5.75x0.50 inches. In Stock.
Edité par Cambridge University Press, Cambridge, 2006
ISBN 10 : 0521617936 ISBN 13 : 9780521617932
Langue: anglais
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Edition originale
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Ajouter au panierPaperback. Etat : new. Paperback. There is a growing need for a more automated system of partitioning data sets into groups, or clusters. For example, digital libraries and the World Wide Web continue to grow exponentially, the ability to find useful information increasingly depends on the indexing infrastructure or search engine. Clustering techniques can be used to discover natural groups in data sets and to identify abstract structures that might reside there, without having any background knowledge of the characteristics of the data. Clustering has been used in a variety of areas, including computer vision, VLSI design, data mining, bio-informatics (gene expression analysis), and information retrieval, to name just a few. This book focuses on a few of the most important clustering algorithms, providing a detailed account of these major models in an information retrieval context. The beginning chapters introduce the classic algorithms in detail, while the later chapters describe clustering through divergences and show recent research for more advanced audiences. This book focuses on a few of the most important clustering algorithms, providing a detailed account of these major models in an information retrieval context. The beginning chapters introduce the classic algorithms in detail, while the later chapters describe clustering through divergences and show recent research for more advanced audiences. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Edité par Cambridge University Press, Cambridge, 2006
ISBN 10 : 0521617936 ISBN 13 : 9780521617932
Langue: anglais
Vendeur : AussieBookSeller, Truganina, VIC, Australie
Edition originale
EUR 75,50
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Ajouter au panierPaperback. Etat : new. Paperback. There is a growing need for a more automated system of partitioning data sets into groups, or clusters. For example, digital libraries and the World Wide Web continue to grow exponentially, the ability to find useful information increasingly depends on the indexing infrastructure or search engine. Clustering techniques can be used to discover natural groups in data sets and to identify abstract structures that might reside there, without having any background knowledge of the characteristics of the data. Clustering has been used in a variety of areas, including computer vision, VLSI design, data mining, bio-informatics (gene expression analysis), and information retrieval, to name just a few. This book focuses on a few of the most important clustering algorithms, providing a detailed account of these major models in an information retrieval context. The beginning chapters introduce the classic algorithms in detail, while the later chapters describe clustering through divergences and show recent research for more advanced audiences. This book focuses on a few of the most important clustering algorithms, providing a detailed account of these major models in an information retrieval context. The beginning chapters introduce the classic algorithms in detail, while the later chapters describe clustering through divergences and show recent research for more advanced audiences. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Edité par Cambridge University Press, 2006
ISBN 10 : 0521617936 ISBN 13 : 9780521617932
Langue: anglais
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
EUR 46,96
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Ajouter au panierEtat : New.
Edité par Cambridge University Press, Cambridge, 2006
ISBN 10 : 0521617936 ISBN 13 : 9780521617932
Langue: anglais
Vendeur : Grand Eagle Retail, Fairfield, OH, Etats-Unis
Edition originale
EUR 58,46
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Ajouter au panierPaperback. Etat : new. Paperback. There is a growing need for a more automated system of partitioning data sets into groups, or clusters. For example, digital libraries and the World Wide Web continue to grow exponentially, the ability to find useful information increasingly depends on the indexing infrastructure or search engine. Clustering techniques can be used to discover natural groups in data sets and to identify abstract structures that might reside there, without having any background knowledge of the characteristics of the data. Clustering has been used in a variety of areas, including computer vision, VLSI design, data mining, bio-informatics (gene expression analysis), and information retrieval, to name just a few. This book focuses on a few of the most important clustering algorithms, providing a detailed account of these major models in an information retrieval context. The beginning chapters introduce the classic algorithms in detail, while the later chapters describe clustering through divergences and show recent research for more advanced audiences. This book focuses on a few of the most important clustering algorithms, providing a detailed account of these major models in an information retrieval context. The beginning chapters introduce the classic algorithms in detail, while the later chapters describe clustering through divergences and show recent research for more advanced audiences. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Edité par Cambridge University Press, 2006
ISBN 10 : 0521617936 ISBN 13 : 9780521617932
Langue: anglais
Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
EUR 50,72
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Ajouter au panierPaperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 339.
Vendeur : Revaluation Books, Exeter, Royaume-Uni
EUR 47,13
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Ajouter au panierPaperback. Etat : Brand New. 1st edition. 205 pages. 8.75x5.75x0.50 inches. In Stock. This item is printed on demand.
Edité par Cambridge University Press, 2007
ISBN 10 : 0521617936 ISBN 13 : 9780521617932
Langue: anglais
Vendeur : moluna, Greven, Allemagne
EUR 54,70
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Ajouter au panierEtat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book focuses on a few of the most important clustering algorithms, providing a detailed account of these major models in an information retrieval context. The beginning chapters introduce the classic algorithms in detail, while the later chapters descr.