東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Clustering methods for big data anal...

Nasraoui, Olfa.

FindBook

Google Book

Amazon

博客來

Clustering methods for big data analytics = techniques, toolboxes and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Clustering methods for big data analytics/ edited by Olfa Nasraoui, Chiheb-Eddine Ben N'Cir.
其他題名:	techniques, toolboxes and applications /
其他作者:	Nasraoui, Olfa.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	ix, 187 p. :ill., digital ;24 cm.
內容註:	Introduction -- Clustering large scale data -- Clustering heterogeneous data -- Distributed clustering methods -- Clustering structured and unstructured data -- Clustering and unsupervised learning for deep learning -- Deep learning methods for clustering -- Clustering high speed cloud, grid, and streaming data -- Extension of partitioning, model based, density based, grid based, fuzzy and evolutionary clustering methods for big data analysis -- Large documents and textual data clustering -- Applications of big data clustering methods -- Clustering multimedia and multi-structured data -- Large-scale recommendation systems and social media systems -- Clustering multimedia and multi-structured data -- Real life applications of big data clustering -- Validation measures for big data clustering methods -- Conclusion.
Contained By:	Springer eBooks
標題:	Big data. -
電子資源:	https://doi.org/10.1007/978-3-319-97864-2
ISBN:	9783319978642

Clustering methods for big data analytics = techniques, toolboxes and applications /
Clustering methods for big data analyticstechniques, toolboxes and applications /[electronic resource] :edited by Olfa Nasraoui, Chiheb-Eddine Ben N'Cir. - Cham :Springer International Publishing :2019. - ix, 187 p. :ill., digital ;24 cm. - Unsupervised and semi-supervised learning,2522-848X. - Unsupervised and semi-supervised learning..

Introduction -- Clustering large scale data -- Clustering heterogeneous data -- Distributed clustering methods -- Clustering structured and unstructured data -- Clustering and unsupervised learning for deep learning -- Deep learning methods for clustering -- Clustering high speed cloud, grid, and streaming data -- Extension of partitioning, model based, density based, grid based, fuzzy and evolutionary clustering methods for big data analysis -- Large documents and textual data clustering -- Applications of big data clustering methods -- Clustering multimedia and multi-structured data -- Large-scale recommendation systems and social media systems -- Clustering multimedia and multi-structured data -- Real life applications of big data clustering -- Validation measures for big data clustering methods -- Conclusion.

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.

ISBN: 9783319978642

Standard No.: 10.1007/978-3-319-97864-2doiSubjects--Topical Terms:

2045508
Big data.

LC Class. No.: QA76.9.B45 / C587 2019

Dewey Class. No.: 005.7

Clustering methods for big data analytics = techniques, toolboxes and applications /
LDR:02788nmm a2200337 a 4500 001 2177587
003 DE-He213
005 20190530173317.0
006 m d
007 cr nn 008maaau
008 191122s2019 gw s 0 eng d
020 $a 9783319978642 $q (electronic bk.)
020 $a 9783319978635 $q (paper)
024 7 $a 10.1007/978-3-319-97864-2 $2 doi
035 $a 978-3-319-97864-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.B45 $b C587 2019
072 7 $a TJK $2 bicssc
072 7 $a TEC041000 $2 bisacsh
072 7 $a TJK $2 thema
082 0 4 $a 005.7 $2 23
090 $a QA76.9.B45 $b C649 2019
245 0 0 $a Clustering methods for big data analytics $h [electronic resource] : $b techniques, toolboxes and applications / $c edited by Olfa Nasraoui, Chiheb-Eddine Ben N'Cir.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a ix, 187 p. : $b ill., digital ; $c 24 cm.
490 1 $a Unsupervised and semi-supervised learning, $x 2522-848X
505 0 $a Introduction -- Clustering large scale data -- Clustering heterogeneous data -- Distributed clustering methods -- Clustering structured and unstructured data -- Clustering and unsupervised learning for deep learning -- Deep learning methods for clustering -- Clustering high speed cloud, grid, and streaming data -- Extension of partitioning, model based, density based, grid based, fuzzy and evolutionary clustering methods for big data analysis -- Large documents and textual data clustering -- Applications of big data clustering methods -- Clustering multimedia and multi-structured data -- Large-scale recommendation systems and social media systems -- Clustering multimedia and multi-structured data -- Real life applications of big data clustering -- Validation measures for big data clustering methods -- Conclusion.
520 $a 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.
650 0 $a Big data. $3 2045508
650 0 $a Cluster analysis. $3 562995
650 0 $a Data mining. $3 562972
650 1 4 $a Communications Engineering, Networks. $3 891094
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Big Data/Analytics. $3 2186785
650 2 4 $a Pattern Recognition. $3 891045
700 1 $a Nasraoui, Olfa. $3 3380846
700 1 $a Ben N'Cir, Chiheb-Eddine. $3 3380847
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Unsupervised and semi-supervised learning. $3 3380848
856 4 0 $u https://doi.org/10.1007/978-3-319-97864-2
950 $a Engineering (Springer-11647)