Link Mining: Models, Algorithms, and Applications
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- Hardcover
- Taschenbuch
- eBook ausgewählt
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Form:Einzelkauf Download
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Sprache:Englisch
213,99 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
16.09.2010
Herausgeber
Philip S. Yu + weitereVerlag
SpringerSeitenzahl
586 (Printausgabe)
Dateigröße
14421 KB
Auflage
2010
Sprache
Englisch
EAN
9781441965158
With the recent flourishing research activities on Web search and mining, social network analysis, information network analysis, information retrieval, link analysis, and structural data mining, research on link mining has been rapidly growing, forming a new field of data mining.
Traditional data mining focuses on "flat" or "isolated" data in which each data object is represented as an independent attribute vector. However, many real-world data sets are inter-connected, much richer in structure, involving objects of heterogeneous types and complex links. Hence, the study of link mining will have a high impact in various important applications such as Web and text mining, social network analysis, collaborative filtering, and bioinformatics.
Link Mining: Models, Algorithms and Applications focuses on the theory and techniques as well as the related applications for link mining, especially from an interdisciplinary point of view. Due to the high popularity of linkage data, extensive applications ranging from governmental organizations to commercial businesses to people's daily life call for exploring the techniques of mining linkage data. This book provides a comprehensive coverage of the link mining models, techniques and applications. Each chapter is contributed from some well known researchers in the field.
Link Mining: Models, Algorithms and Applications is designed for researchers, teachers, and advanced-level students in computer science. This book is also suitable for practitioners in industry.
Traditional data mining focuses on "flat" or "isolated" data in which each data object is represented as an independent attribute vector. However, many real-world data sets are inter-connected, much richer in structure, involving objects of heterogeneous types and complex links. Hence, the study of link mining will have a high impact in various important applications such as Web and text mining, social network analysis, collaborative filtering, and bioinformatics.
Link Mining: Models, Algorithms and Applications focuses on the theory and techniques as well as the related applications for link mining, especially from an interdisciplinary point of view. Due to the high popularity of linkage data, extensive applications ranging from governmental organizations to commercial businesses to people's daily life call for exploring the techniques of mining linkage data. This book provides a comprehensive coverage of the link mining models, techniques and applications. Each chapter is contributed from some well known researchers in the field.
Link Mining: Models, Algorithms and Applications is designed for researchers, teachers, and advanced-level students in computer science. This book is also suitable for practitioners in industry.
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