| 000 | 00000nam u2200205 a 4500 | |
| 001 | 000045972701 | |
| 005 | 20190304095343 | |
| 008 | 190228s2019 sz a b 001 0 eng d | |
| 020 | ▼a 9783319978635 (hbk.) | |
| 020 | ▼a 9783319978642 (ebk.) | |
| 040 | ▼a 211009 ▼c 211009 ▼d 211009 | |
| 082 | 0 4 | ▼a 006.312 ▼2 23 |
| 084 | ▼a 006.312 ▼2 DDCK | |
| 090 | ▼a 006.312 ▼b C649 | |
| 245 | 0 0 | ▼a Clustering methods for big data analytics : ▼b techniques, toolboxes and applications / ▼c Olfa Nasraoui, Chiheb-Eddine Ben N'Cir, editors. |
| 260 | ▼a Cham : ▼b Springer, ▼c c2019. | |
| 300 | ▼a ix, 187 p. : ▼b ill. (some col.) ; ▼c 25 cm. | |
| 490 | 1 | ▼a Unsupervised and semi-supervised learning |
| 504 | ▼a Includes bibliographical references and index. | |
| 650 | 0 | ▼a Big data. |
| 650 | 0 | ▼a Cluster analysis. |
| 650 | 0 | ▼a Data mining. |
| 650 | 0 | ▼a Artificial intelligence. |
| 650 | 0 | ▼a Business mathematics & systems. |
| 650 | 0 | ▼a Pattern recognition. |
| 650 | 0 | ▼a Communications engineering ▼x telecommunications. |
| 700 | 1 | ▼a Nasraoui, Olfa. |
| 700 | 1 | ▼a Ben N'Cir, Chiheb-Eddine. |
| 830 | 0 | ▼a Unsupervised and semi-supervised learning. |
| 945 | ▼a KLPA |
Holdings Information
| No. | Location | Call Number | Accession No. | Availability | Due Date | Make a Reservation | Service |
|---|---|---|---|---|---|---|---|
| No. 1 | Location Main Library/Western Books/ | Call Number 006.312 C649 | Accession No. 111805559 | Availability Available | Due Date | Make a Reservation | Service |
Contents information
Book Introduction
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.
New feature
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.Information Provided By: :
Table of Contents
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.
Information Provided By: :