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Record Nr. |
UNINA9910254821103321 |
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Autore |
Shi Chuan |
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Titolo |
Heterogeneous Information Network Analysis and Applications / / by Chuan Shi, Philip S. Yu |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
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ISBN |
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Edizione |
[1st ed. 2017.] |
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Descrizione fisica |
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1 online resource (IX, 227 p. 62 illus., 53 illus. in color.) |
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Collana |
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Data Analytics, , 2520-1867 |
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Disciplina |
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Soggetti |
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Data mining |
Artificial intelligence |
Pattern recognition systems |
Telecommunication |
Computer networks |
Data Mining and Knowledge Discovery |
Artificial Intelligence |
Automated Pattern Recognition |
Communications Engineering, Networks |
Computer Communication Networks |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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1. Introduction -- 2. Summarization of the developments -- 3.Uniform relevance measure of heterogeneous objects -- 4. Path based Ranking -- 5. Ranking based Clustering -- 6. Recommendation with heterogeneous information -- 7. Information fusion with heterogeneous network -- 8. Prototype system -- 9. Future research directions -- 10. Conclusion. |
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Sommario/riassunto |
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This book offers researchers an understanding of the fundamental issues and a good starting point to work on this rapidly expanding field. It provides a comprehensive survey of current developments of heterogeneous information network. It also presents the newest research in applications of heterogeneous information networks to similarity search, ranking, clustering, recommendation. This information will help researchers to understand how to analyze |
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networked data with heterogeneous information networks. Common data mining tasks are explored, including similarity search, ranking, and recommendation. The book illustrates some prototypes which analyze networked data. Professionals and academics working in data analytics, networks, machine learning, and data mining will find this content valuable. It is also suitable for advanced-level students in computer science who are interested in networking orpattern recognition. . |
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