Screen LibraryLink mining：models, algorithms, and applications
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| Corporate Authors: | |
|---|---|
| Group Author: | ; ; |
| Published: |
Springer,
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| Publisher Address: | New York |
| Publication Dates: | c2010. |
| Literature type: | Book |
| Language: | English |
| Subjects: | |
| Online Access: |
http://dx.doi.org/10.1007/978-1-4419-6515-8 |
| Carrier Form: | 1 online resource (xiii, 586 p.): ill. |
| ISBN: |
9781441965158 (electronic bk.) 1441965157 (electronic bk.) |
| Index Number: | TP311 |
| CLC: | TP311.131 |
| Contents: |
Includes bibliographical references and index. Preface; Contents; Contributors; Part I Link-Based Clustering; 1 Machine Learning Approaches to Link-Based Clustering; 2 Scalable Link-Based Similarity Computation and Clustering; 3 Community Evolution and Change Point Detection in Time-Evolving Graphs; Part II Graph Mining and Community Analysis; 4 A Survey of Link Mining Tasks for Analyzing Noisy and Incomplete Networks; 5 Markov Logic: A Language and Algorithms for Link Mining; 6 Understanding Group Structures and Properties in Social Media; 7 Time Sensitive Ranking with Application to Publication Search. This book presents in-depth surveys and systematic discussions on models, algorithms and applications for link mining. Link mining is an important field of data mining. Traditional data mining focuses on 'flat' data in which each data object is represented as a fixed-length attribute vector. However, many real-world data sets are much richer in structure, involving objects of multiple types that are related to each other. Hence, recently link mining has become an emerging field of data mining, which has a high impact in various important applications such as text mining, social network analysi. |