Relational Data Clustering /

A culmination of the authors' years of extensive research on this topic, Relational Data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering. It describes theoretic models and algorithms and, through examples, shows how to a...

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Bibliographic Details
Main Authors: Long, Bo (Author), Zhang, Zhongfei (Author), Yu, Philip (Author)
Corporate Author: Safari, an O'Reilly Media Company
Other Authors: Yu, Philip S.
Format: Electronic eBook
Language:Inglés
Published: [Place of publication not identified] : Chapman and Hall/CRC, 2010.
Edition:1st edition.
Series:Chapman & Hall/CRC data mining and knowledge discovery series.
Subjects:
Data mining.
Relational databases.
Cluster analysis.
Exploration de données (Informatique)
Bases de données relationnelles.
Classification automatique (Statistique)
Cluster analysis
Data mining
Relational databases
Online Access:Texto completo (Requiere registro previo con correo institucional)
Description
Summary:A culmination of the authors' years of extensive research on this topic, Relational Data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering. It describes theoretic models and algorithms and, through examples, shows how to apply these models and algorithms to solve real-world problems. After defining the field, the book introduces different types of model formulations for relational data clustering, presents various algorithms for the corresponding models, and demonstrates applications of the models and algorithms through extensive experimental results. The authors cover six topics of relational data clustering: Clustering on bi-type heterogeneous relational data Multi-type heterogeneous relational data Homogeneous relational data clustering Clustering on the most general case of relational data Individual relational clustering framework Recent research on evolutionary clustering This book focuses on both practical algorithm derivation and theoretical framework construction for relational data clustering. It provides a complete, self-contained introduction to advances in the field.
Physical Description:1 online resource (216 pages).
ISBN:9781420072624
1420072625

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