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Relational Data Clustering: Models, Algorithms, and Applications
ISBN: 9780429140853
Platform/Publisher: Taylor & Francis / Chapman and Hall/CRC
Digital rights: Users: Unlimited; Printing: Unlimited; Download: Unlimited

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.

Bo Long is a scientist at Yahoo! Labs in Sunnyvale, California.

Zhongfei Zhang is an associate professor in the computer science department at the State University of New York in Binghamton.

Philip S. Yu is a professor in the computer science department and the Wexler Chair in Information Technology at the University of Illinois in Chicago.