Relational Data Clustering

Presents a comprehensive overview of relational data clustering in data mining research. This book reflects the emergence of relational data clustering as an important field of data clustering, with applications in text mining, social network analysis, collaborative filtering, and bioinformatics.

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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.