Relational data clustering : models, algorithms, and applications / / Bo Long, Zhongfei Zhang, Philip S. Yu

• Record Nr.: UNINA9910814076703321
• Pubbl/distr/stampa: Boca Raton, : Chapman & Hall/CRC, c2010
• ISBN: 0-429-14085-1; 1-282-90231-8; 9786612902314; 1-4200-7262-5
• Edizione: [1st edition]
• Descrizione fisica: 1 online resource (214 p.)
• Collana: Chapman & Hall/CRC data mining and knowledge discovery series
• Altri autori (Persone): LongBo; YuPhilip S; ZhangZhongfei
• Disciplina: 005.75/6; 006.312
• Soggetti: Cluster analysis; Data mining; Relational databases
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Front cover; Contents; List of Tables; List of Figures; Preface; Chapter 1: Introduction; Part I: Models; Chapter 2: Co-Clustering; Chapter 3: Heterogeneous Relational Data Clustering; Chapter 4: Homogeneous Relational Data Clustering; Chapter 5: General Relational Data Clustering; Chapter 6: Multiple-View Relational Data Clustering; Chapter 7: Evolutionary Data Clustering; Part II: Algorithms; Chapter 8: Co-Clustering; Chapter 9: Heterogeneous Relational Data Clustering; Chapter 10: Homogeneous Relational Data Clustering; Chapter 11: General Relational Data Clustering; Chapter 12: Multiple-View Relational Data ClusteringChapter 13: Evolutionary Data Clustering; Part III: Applications; Chapter 14: Co-Clustering; Chapter 15: Heterogeneous Relational Data Clustering; Chapter 16: Homogeneous Relational Data Clustering; Chapter 17: General Relational Data Clustering; Chapter 18: Multiple-View and Evolutionary Data Clustering; Part IV: Summary; References; Index; Back ; cover
• Sommario/riassunto: This is the first book available that presents a comprehensive overview of relational data clustering in data mining research. The book reflects the recent emergence of relational data clustering as an important field of data clustering, with applications in text mining, social network analysis, collaborative filtering, and bioinformatics. It presents an in-depth, systematic discussion of the models, algorithms, and applications for relational data clustering. The book also covers recently emerging models in relational data clustering, including graph-based models, matrix factorization-based m