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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 realworld 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 bitype heterogeneous relational dataMultitype heterogeneous relational dataHomogeneous relational data clusteringClustering on the most general case of relational dataIndividual relational clustering frameworkRecent research on evolutionary clusteringThis book focuses on both practical algorithm derivation and theoretical framework construction for relational data clustering. It provides a complete, selfcontained introduction to advances in the field.
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