Relational Data Clustering : Models, Algorithms, and Applications

A culmination of the authors’ years of extensive research on the topic, this book provides a complete, self-contained introduction to advances in relational data clustering. It presents a novel theoretical framework as well as the most state-of-the-art models for relational data clustering. The authors also describe a family of new algorithms for various relational clustering problems arising in a wide range of important applications. Concepts and theory are explained through detailed application examples and the book includes a comprehensive reference list of related literature.

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:

1. Clustering on bi-type heterogeneous relational data



2. Multi-type heterogeneous relational data



3. Homogeneous relational data clustering



4. Clustering on the most general case of relational data



5. Individual relational clustering framework



6. 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.