posted by user: chihheb || 3421 views || tracked by 1 users: [display]

Cluster-BigData 2018 : Call for Springer book Chapters: Clustering methods for Big Data Analytics: techniques, toolboxes and applications, Springer (USA)

FacebookTwitterLinkedInGoogle

Link: https://easychair.org/cfp/Cluster-BigData2018
 
When N/A
Where USA
Abstract Registration Due Dec 5, 2017
Submission Deadline Jan 21, 2018
Notification Due Mar 24, 2018
Final Version Due Apr 22, 2018
Categories    clustering   unsupervised learning   big data analysis   large scale data
 

Call For Papers



Dear colleagues,

We would like to invite you to contribute a chapter for our upcoming book entitled “Clustering Methods for Big Data Analytics: Techniques, toolboxes and applications” to be published by Springer sometime in 2018

Book submission website: https://easychair.org/cfp/Cluster-BigData2018

Below is a short description of the book:

=============================================================================================================================

This book will present recent research advances in designing efficient clustering methods and tools for analyzing Big data and their innovative applications in contemporary AI systems, such as information retrieval, text mining, recommender systems, smart cities, Internet of things, digital and mobile health, human-robot interaction, social network analysis, etc.

The large volume and variety of data, being generated at an accelerated velocity, creates an important opportunity and challenge for humans and organizations. Data has become the lifeblood of today’s knowledge-driven economy and society. Unfortunately, conventional unsupervised learning techniques, especially clustering, face tremendous challenges when mining such data due to its high complexity, heterogeneity, large volume and rapid generation. This raises exciting challenges for researchers to design new scalable and efficient clustering methods and tools that are able to extract valuable information from data.


The goal of this book is to provide a coverage of recent developments in big data clustering methods, tools, frameworks, applications, representation, visualization, and validation measures for analyzing Big Data.

Topics of interest include, but are not limited to:
● 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
● New Extensions of partitioning, model based, density based, grid based, fuzzy and evolutionary clustering algorithms for Big data analysis
● Clustering large unstructured, and text data
● Applications of Big data clustering methods to advanced manufacturing
● Application of clustering to smart cities and Internet of Things
● Clustering Multimedia and multi-structured Data
● Semi-supervised clustering
● Clustering data streams
● Application of clustering for Large-scale Recommendation Systems
● Application of clustering for mining Social Media Systems
● Validation measures for evaluating Big data clustering results
● Visualization of clusters in Big Data
● New clusterings algorithms for sparse, high, dimensional and noisy data
● New toolboxes for clustering mixed types and/or high dimensional and/or large scale data
● New clustering algorithms on Big Data frameworks: Hadoop, Spark, etc


=============================================================================================================================

Important Dates

Submission of abstracts: December 05 , 2017
Notification of initial editorial decisions: December 25, 2017
Submission of full-length chapters: January 21, 2018
Notification of final editorial decisions: March 24, 2018
Submission of revised chapters: April 22, 2018




=============================================================================================================================

Submissions



All submissions should be done via EasyChair:
https://easychair.org/conferences/?conf=clusterbigdata2018
Original artwork and a signed copyright release form will be required for all accepted chapters. For author instructions, please visit:
http://www.springer.com/gp/authors-editors/book-authors-editors/book-manuscript-guidelines

It is especially important that you use the following Springer book template :

http://www.springer.com/cda/content/document/cda_downloaddocument/svmult.zip?SGWID=0-0-45-491899-0



Feel free to contact the book editors via email (olfa.nasraoui@gmail.com and chiheb.benncir@gmail.com) regarding your chapter ideas.


Editors

Professor Olfa Nasraoui,
University of Louisville, Louisville, USA

&
Dr. Chiheb-Eddine Ben N’Cir, LARODEC Laboratory,
University of Tunis, ESEN, University of Mannouba, Tunisia

Related Resources

Edited Book in Springer-Verlag 2022   Call for Book Chapters-Machine Learning and Deep Learning for Time Series Processing and Analysis
IncrLearn 2022   Incremental classification and clustering, concept drift, novelty detection, active learning in big/fast data context
SF 2022   Call For Springer BOOK Chapters: Green Finance Instruments, Fintech, and Investment Strategies: An Analysis of Sustainable Portfolio Management in the Post-COVID 19 Era
cluster 2022   IEEE Cluster Conference
ML&DataAnalytics 2022   Call for Springer book Chapters ''Machine learning and data analytics for solving business problems: methods, applications, and case studies.'', Springer (USA)
SEN 2022   Social Epidemic Network (SEN), Call for Book Chapters
IEEE BigData 2022   2022 IEEE International Conference on Big Data
CCGrid 2022   CCGrid 2022 The 22nd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing
HPCCT--ACM, EI, Scopus 2022   ACM--2022 6th High Performance Computing and Cluster Technologies Conference (HPCCT 2022)--EI Compendex, Scopus
CyberHunt 2022   IEEE BigData Workshop on Cyber Threat Intelligence and Hunting