| 書目名稱 | Clustering Methods for Big Data Analytics | | 副標題 | Techniques, Toolboxe | | 編輯 | Olfa Nasraoui,Chiheb-Eddine Ben N‘Cir | | 視頻video | http://file.papertrans.cn/229/228548/228548.mp4 | | 概述 | Includes the most recent and innovative advances in Big Data Clustering.Describes recent tools, techniques, and frameworks for Big Data Analytics.Introduces surveys, applications and case studies of B | | 叢書名稱 | Unsupervised and Semi-Supervised Learning | | 圖書封面 |  | | 描述 | .This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation.. | | 出版日期 | Book 2019 | | 關(guān)鍵詞 | Clustering large scale data; Clustering heterogeneous data; Deep learning methods for clustering; Appli | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-97864-2 | | isbn_softcover | 978-3-030-07419-7 | | isbn_ebook | 978-3-319-97864-2Series ISSN 2522-848X Series E-ISSN 2522-8498 | | issn_series | 2522-848X | | copyright | Springer Nature Switzerland AG 2019 |
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