Link mining: Models, algorithms, and applications

Philip S. Yu, Christos Faloutsos, Jiawei Han

Research output: Book/ReportBook

Abstract

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.

Original languageEnglish (US)
PublisherSpringer New York
Number of pages586
Volume9781441965158
ISBN (Electronic)9781441965158
ISBN (Print)9781441965141
DOIs
StatePublished - Jan 1 2010

Fingerprint

Data Mining
Information Storage and Retrieval
Social Support
Research Personnel
Information Services
Computational Biology
Research
Industry
Organizations
Students

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Link mining : Models, algorithms, and applications. / Yu, Philip S.; Faloutsos, Christos; Han, Jiawei.

Springer New York, 2010. 586 p.

Research output: Book/ReportBook

Yu, PS, Faloutsos, C & Han, J 2010, Link mining: Models, algorithms, and applications. vol. 9781441965158, Springer New York. https://doi.org/10.1007/978-1-4419-6515-8
Yu, Philip S. ; Faloutsos, Christos ; Han, Jiawei. / Link mining : Models, algorithms, and applications. Springer New York, 2010. 586 p.
@book{3d96900f565749f58ab15c4f225ac264,
title = "Link mining: Models, algorithms, and applications",
abstract = "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.",
author = "Yu, {Philip S.} and Christos Faloutsos and Jiawei Han",
year = "2010",
month = "1",
day = "1",
doi = "10.1007/978-1-4419-6515-8",
language = "English (US)",
isbn = "9781441965141",
volume = "9781441965158",
publisher = "Springer New York",

}

TY - BOOK

T1 - Link mining

T2 - Models, algorithms, and applications

AU - Yu, Philip S.

AU - Faloutsos, Christos

AU - Han, Jiawei

PY - 2010/1/1

Y1 - 2010/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84919835260&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84919835260&partnerID=8YFLogxK

U2 - 10.1007/978-1-4419-6515-8

DO - 10.1007/978-1-4419-6515-8

M3 - Book

AN - SCOPUS:84919835260

SN - 9781441965141

VL - 9781441965158

BT - Link mining

PB - Springer New York

ER -