Structure in Complex Networks
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- Engels
- Hardcover
- 9783540878322
- 24 november 2008
- 151 pagina's
Samenvatting
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.
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. 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.
Productspecificaties
Inhoud
- Taal
- en
- Bindwijze
- Hardcover
- Oorspronkelijke releasedatum
- 24 november 2008
- Aantal pagina's
- 151
- Illustraties
- Nee
Betrokkenen
- Hoofdauteur
- Jörg Reichardt
- Tweede Auteur
- J. Rg Reichardt
- Hoofduitgeverij
- Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Overige kenmerken
- Editie
- 2009 ed.
- Extra groot lettertype
- Nee
- Product breedte
- 155 mm
- Product lengte
- 235 mm
- Studieboek
- Ja
- Verpakking breedte
- 163 mm
- Verpakking hoogte
- 41 mm
- Verpakking lengte
- 244 mm
- Verpakkingsgewicht
- 447 g
EAN
- EAN
- 9783540878322
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