Clustering Methods for Big Data Analytics [electronic resource] Techniques, Toolboxes and Applications / edited by Olfa Nasraoui, Chiheb-Eddine Ben N'Cir.
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 dete...
| Uniform Title: | Unsupervised and Semi-Supervised Learning,
2522-8498 |
|---|---|
| Corporate Author: | |
| Other Authors: | |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2019.
|
| Edition: | 1st ed. 2019. |
| Series: | Unsupervised and Semi-Supervised Learning,
|
| Subjects: | |
| Online Access: | |
| Variant Title: |
Clustering Methods for Big Data Analytics: Techniques, Toolboxes and Applications |
| Format: | Electronic eBook |
MARC
| LEADER | 00000nam a22000003i 4500 | ||
|---|---|---|---|
| 001 | ebs19319107e | ||
| 003 | EBZ | ||
| 006 | m o d |||||| | ||
| 007 | cr|unu|||||||| | ||
| 008 | 181027s2019 sz | o |||| 0|eng d | ||
| 020 | |z 9783319978635 | ||
| 020 | |a 9783319978642 (online) | ||
| 035 | |a (EBZ)ebs19319107e | ||
| 040 | |d EBZ | ||
| 042 | |a msc | ||
| 050 | 4 | |a TK5101-5105.9 | |
| 245 | 1 | 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. |
| 246 | 2 | |a Clustering Methods for Big Data Analytics: Techniques, Toolboxes and Applications | |
| 250 | |a 1st ed. 2019. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2019. | |
| 490 | 1 | |a Unsupervised and Semi-Supervised Learning, |x 2522-8498 | |
| 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 Telecommunication. | |
| 650 | 0 | |a Computational intelligence. | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Quantitative research. | |
| 650 | 0 | |a Pattern recognition systems. | |
| 700 | 1 | |a Nasraoui, Olfa. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Ben N'Cir, Chiheb-Eddine. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Engineering eBooks 2019 English/International |d Springer Nature | |
| 776 | 0 | 8 | |i Printed edition: |z 9783319978635 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319978659 |
| 776 | 0 | 8 | |i Printed edition: |z 9783030074197 |
| 776 | 1 | |t Clustering Methods for Big Data Analytics | |
| 830 | 0 | |a Unsupervised and Semi-Supervised Learning, |x 2522-8498 | |
| 856 | 4 | 0 | |y Access Content Online(from Springer Engineering eBooks 2019 English/International) |u https://ezproxy.msu.edu/login?url=https://link.springer.com/10.1007/978-3-319-97864-2 |z Springer Engineering eBooks 2019 English/International: 2019 |