Heterogeneous information network analysis and applications
- 作者： Shi, Chuan, author.
- 其他作者：
- 其他題名：
- Data analytics.
- 出版： Cham : Springer International Publishing :Imprint: Springer
- 叢書名： Data analytics,
- 主題： Data mining. , Computer networks. , Computer Science. , Data Mining and Knowledge Discovery. , Artificial Intelligence (incl. Robotics) , Pattern Recognition. , Communications Engineering, Networks. , Computer Communication Networks.
- ISBN： 9783319562124 (electronic bk.) 、 9783319562117 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 內容註: 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.
- 摘要註: 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 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 or pattern recognition.
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讀者標籤：
- 系統號： 005399495 | 機讀編目格式
館藏資訊
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 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 or pattern recognition.