Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
Condition: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
US$ 60.88
Convert currencyQuantity: Over 20 available
Add to basketCondition: New.
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
US$ 59.54
Convert currencyQuantity: Over 20 available
Add to basketCondition: New.
Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2008
ISBN 10: 3540878327 ISBN 13: 9783540878322
Language: English
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Hardcover. Condition: new. Hardcover. In the modern world of gigantic datasets, which scientists and practioners of all fields of learning are confronted with, the availability of robust, scalable and easy-to-use methods for pattern recognition and data mining are of paramount importance, so as to be able to cope with the avalanche of data in a meaningful way. This concise and pedagogical research monograph introduces the reader to two specific aspects - clustering techniques and dimensionality reduction - in the context of complex network analysis. The first chapter provides a short introduction into relevant graph theoretical notation; chapter 2 then reviews and compares a number of cluster definitions from different fields of science. In the subsequent chapters, a first-principles approach to graph clustering in complex networks is developed using methods from statistical physics and the reader will learn, that even today, this field significantly contributes to the understanding and resolution of the related statistical inference issues. Finally, an application chapter examines real-world networks from the economic realm to show how the network clustering process can be used to deal with large, sparse datasets where conventional analyses fail. In the modern world of gigantic datasets, which scientists and practioners of all fields of learning are confronted with, the availability of robust, scalable and easy-to-use methods for pattern recognition and data mining are of paramount importance, so as to be able to cope with the avalanche of data in a meaningful way. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
US$ 69.06
Convert currencyQuantity: Over 20 available
Add to basketCondition: As New. Unread book in perfect condition.
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
US$ 74.64
Convert currencyQuantity: Over 20 available
Add to basketCondition: New.
Seller: Ria Christie Collections, Uxbridge, United Kingdom
US$ 67.92
Convert currencyQuantity: Over 20 available
Add to basketCondition: New. In.
Seller: Ria Christie Collections, Uxbridge, United Kingdom
US$ 70.25
Convert currencyQuantity: Over 20 available
Add to basketCondition: New. In.
US$ 65.70
Convert currencyQuantity: 10 available
Add to basketPF. Condition: New.
Condition: New. pp. 168.
Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2010
ISBN 10: 3642099653 ISBN 13: 9783642099656
Language: English
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
First Edition
Paperback. Condition: new. Paperback. In the modern world of gigantic datasets, which scientists and practioners of all fields of learning are confronted with, the availability of robust, scalable and easy-to-use methods for pattern recognition and data mining are of paramount importance, so as to be able to cope with the avalanche of data in a meaningful way. This concise and pedagogical research monograph introduces the reader to two specific aspects - clustering techniques and dimensionality reduction - in the context of complex network analysis. The first chapter provides a short introduction into relevant graph theoretical notation; chapter 2 then reviews and compares a number of cluster definitions from different fields of science. In the subsequent chapters, a first-principles approach to graph clustering in complex networks is developed using methods from statistical physics and the reader will learn, that even today, this field significantly contributes to the understanding and resolution of the related statistical inference issues. Finally, an application chapter examines real-world networks from the economic realm to show how the network clustering process can be used to deal with large, sparse datasets where conventional analyses fail. In the modern world of gigantic datasets, which scientists and practioners of all fields of learning are confronted with, the availability of robust, scalable and easy-to-use methods for pattern recognition and data mining are of paramount importance, so as to be able to cope with the avalanche of data in a meaningful way. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
US$ 70.24
Convert currencyQuantity: Over 20 available
Add to basketCondition: New.
US$ 77.04
Convert currencyQuantity: Over 20 available
Add to basketCondition: As New. Unread book in perfect condition.
Published by Springer Berlin Heidelberg, 2008
ISBN 10: 3540878327 ISBN 13: 9783540878322
Language: English
Seller: moluna, Greven, Germany
US$ 76.90
Convert currencyQuantity: Over 20 available
Add to basketGebunden. Condition: New. In the modern world of gigantic datasets, which scientists and practioners of all fields of learning are confronted with, the availability of robust, scalable and easy-to-use methods for pattern recognition and data mining are of paramount importance, so.
Published by Springer Berlin Heidelberg, 2010
ISBN 10: 3642099653 ISBN 13: 9783642099656
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
US$ 64.14
Convert currencyQuantity: 1 available
Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - In the modern world of gigantic datasets, which scientists and practioners of all fields of learning are confronted with, the availability of robust, scalable and easy-to-use methods for pattern recognition and data mining are of paramount importance, so as to be able to cope with the avalanche of data in a meaningful way.This concise and pedagogical research monograph introduces the reader to two specific aspects - clustering techniques and dimensionality reduction - in the context of complex network analysis. The first chapter provides a short introduction into relevant graph theoretical notation; chapter 2 then reviews and compares a number of cluster definitions from different fields of science. In the subsequent chapters, a first-principles approach to graph clustering in complex networks is developed using methods from statistical physics and the reader will learn, thateven today, this field significantly contributes to the understanding and resolution of the related statistical inference issues. Finally, an application chapter examines real-world networks from the economic realm to show how the network clustering process can be used to deal with large, sparse datasets where conventional analyses fail.
Published by Springer, Berlin, Springer Berlin Heidelberg, Springer, 2008
ISBN 10: 3540878327 ISBN 13: 9783540878322
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
US$ 69.19
Convert currencyQuantity: 2 available
Add to basketBuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - In the modern world of gigantic datasets, which scientists and practioners of all fields of learning are confronted with, the availability of robust, scalable and easy-to-use methods for pattern recognition and data mining are of paramount importance, so as to be able to cope with the avalanche of data in a meaningful way.This concise and pedagogical research monograph introduces the reader to two specific aspects - clustering techniques and dimensionality reduction - in the context of complex network analysis. The first chapter provides a short introduction into relevant graph theoretical notation; chapter 2 then reviews and compares a number of cluster definitions from different fields of science. In the subsequent chapters, a first-principles approach to graph clustering in complex networks is developed using methods from statistical physics and the reader will learn, thateven today, this field significantly contributes to the understanding and resolution of the related statistical inference issues. Finally, an application chapter examines real-world networks from the economic realm to show how the network clustering process can be used to deal with large, sparse datasets where conventional analyses fail.
Published by Springer-Verlag GmbH, 2008
ISBN 10: 3540878327 ISBN 13: 9783540878322
Language: English
Seller: Buchpark, Trebbin, Germany
US$ 32.63
Convert currencyQuantity: 1 available
Add to basketCondition: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher.
Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2008
ISBN 10: 3540878327 ISBN 13: 9783540878322
Language: English
Seller: AussieBookSeller, Truganina, VIC, Australia
US$ 121.04
Convert currencyQuantity: 1 available
Add to basketHardcover. Condition: new. Hardcover. In the modern world of gigantic datasets, which scientists and practioners of all fields of learning are confronted with, the availability of robust, scalable and easy-to-use methods for pattern recognition and data mining are of paramount importance, so as to be able to cope with the avalanche of data in a meaningful way. This concise and pedagogical research monograph introduces the reader to two specific aspects - clustering techniques and dimensionality reduction - in the context of complex network analysis. The first chapter provides a short introduction into relevant graph theoretical notation; chapter 2 then reviews and compares a number of cluster definitions from different fields of science. In the subsequent chapters, a first-principles approach to graph clustering in complex networks is developed using methods from statistical physics and the reader will learn, that even today, this field significantly contributes to the understanding and resolution of the related statistical inference issues. Finally, an application chapter examines real-world networks from the economic realm to show how the network clustering process can be used to deal with large, sparse datasets where conventional analyses fail. In the modern world of gigantic datasets, which scientists and practioners of all fields of learning are confronted with, the availability of robust, scalable and easy-to-use methods for pattern recognition and data mining are of paramount importance, so as to be able to cope with the avalanche of data in a meaningful way. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Seller: Mispah books, Redhill, SURRE, United Kingdom
US$ 127.19
Convert currencyQuantity: 1 available
Add to basketPaperback. Condition: Like New. Like New. book.
Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2010
ISBN 10: 3642099653 ISBN 13: 9783642099656
Language: English
Seller: AussieBookSeller, Truganina, VIC, Australia
First Edition
US$ 149.37
Convert currencyQuantity: 1 available
Add to basketPaperback. Condition: new. Paperback. In the modern world of gigantic datasets, which scientists and practioners of all fields of learning are confronted with, the availability of robust, scalable and easy-to-use methods for pattern recognition and data mining are of paramount importance, so as to be able to cope with the avalanche of data in a meaningful way. This concise and pedagogical research monograph introduces the reader to two specific aspects - clustering techniques and dimensionality reduction - in the context of complex network analysis. The first chapter provides a short introduction into relevant graph theoretical notation; chapter 2 then reviews and compares a number of cluster definitions from different fields of science. In the subsequent chapters, a first-principles approach to graph clustering in complex networks is developed using methods from statistical physics and the reader will learn, that even today, this field significantly contributes to the understanding and resolution of the related statistical inference issues. Finally, an application chapter examines real-world networks from the economic realm to show how the network clustering process can be used to deal with large, sparse datasets where conventional analyses fail. In the modern world of gigantic datasets, which scientists and practioners of all fields of learning are confronted with, the availability of robust, scalable and easy-to-use methods for pattern recognition and data mining are of paramount importance, so as to be able to cope with the avalanche of data in a meaningful way. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Published by Springer, Berlin, Springer Berlin Heidelberg, Springer Nov 2008, 2008
ISBN 10: 3540878327 ISBN 13: 9783540878322
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
US$ 64.14
Convert currencyQuantity: 2 available
Add to basketBuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In the modern world of gigantic datasets, which scientists and practioners of all fields of learning are confronted with, the availability of robust, scalable and easy-to-use methods for pattern recognition and data mining are of paramount importance, so as to be able to cope with the avalanche of data in a meaningful way.This concise and pedagogical research monograph introduces the reader to two specific aspects - clustering techniques and dimensionality reduction - in the context of complex network analysis. The first chapter provides a short introduction into relevant graph theoretical notation; chapter 2 then reviews and compares a number of cluster definitions from different fields of science. In the subsequent chapters, a first-principles approach to graph clustering in complex networks is developed using methods from statistical physics and the reader will learn, thateven today, this field significantly contributes to the understanding and resolution of the related statistical inference issues. Finally, an application chapter examines real-world networks from the economic realm to show how the network clustering process can be used to deal with large, sparse datasets where conventional analyses fail. 151 pp. Englisch.
Published by Springer Berlin Heidelberg Okt 2010, 2010
ISBN 10: 3642099653 ISBN 13: 9783642099656
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
US$ 64.14
Convert currencyQuantity: 2 available
Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In the modern world of gigantic datasets, which scientists and practioners of all fields of learning are confronted with, the availability of robust, scalable and easy-to-use methods for pattern recognition and data mining are of paramount importance, so as to be able to cope with the avalanche of data in a meaningful way.This concise and pedagogical research monograph introduces the reader to two specific aspects - clustering techniques and dimensionality reduction - in the context of complex network analysis. The first chapter provides a short introduction into relevant graph theoretical notation; chapter 2 then reviews and compares a number of cluster definitions from different fields of science. In the subsequent chapters, a first-principles approach to graph clustering in complex networks is developed using methods from statistical physics and the reader will learn, thateven today, this field significantly contributes to the understanding and resolution of the related statistical inference issues. Finally, an application chapter examines real-world networks from the economic realm to show how the network clustering process can be used to deal with large, sparse datasets where conventional analyses fail. 168 pp. Englisch.
Seller: Majestic Books, Hounslow, United Kingdom
US$ 85.15
Convert currencyQuantity: 4 available
Add to basketCondition: New. Print on Demand pp. 168 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.
Seller: Biblios, Frankfurt am main, HESSE, Germany
US$ 92.94
Convert currencyQuantity: 4 available
Add to basketCondition: New. PRINT ON DEMAND pp. 168.
Published by Springer Berlin Heidelberg, 2010
ISBN 10: 3642099653 ISBN 13: 9783642099656
Language: English
Seller: moluna, Greven, Germany
US$ 58.00
Convert currencyQuantity: Over 20 available
Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. In the modern world of gigantic datasets, which scientists and practioners of all fields of learning are confronted with, the availability of robust, scalable and easy-to-use methods for pattern recognition and data mining are of paramount importance, so.
Published by Springer Berlin Heidelberg, Springer Okt 2010, 2010
ISBN 10: 3642099653 ISBN 13: 9783642099656
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
US$ 64.14
Convert currencyQuantity: 1 available
Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In the modern world of gigantic datasets, which scientists and practioners of all fields of learning are confronted with, the availability of robust, scalable and easy-to-use methods for pattern recognition and data mining are of paramount importance, so as to be able to cope with the avalanche of data in a meaningful way. This concise and pedagogical research monograph introduces the reader to two specific aspects - clustering techniques and dimensionality reduction - in the context of complex network analysis. The first chapter provides a short introduction into relevant graph theoretical notation; chapter 2 then reviews and compares a number of cluster definitions from different fields of science. In the subsequent chapters, a first-principles approach to graph clustering in complex networks is developed using methods from statistical physics and the reader will learn, that even today, this field significantly contributes to the understanding and resolution of the related statistical inference issues. Finally, an application chapter examines real-world networks from the economic realm to show how the network clustering process can be used to deal with large, sparse datasets where conventional analyses fail.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 168 pp. Englisch.