Cluster-BigData 2018: Clustering Methods for Big Data Analytics |
| Website | http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=69016©ownerid=104393 |
| Submission link | https://easychair.org/conferences/?conf=clusterbigdata2018 |
| Abstract registration deadline | November 10, 2017 |
| Extension of abstract registration deadline | December 5, 2017 |
| Submission deadline | January 21, 2018 |
Call for Springer Book Chapters:
"Clustering Methods for Big Data Analytics: Techniques, toolboxes and applications"
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-enabled systems, such as information retrieval, text mining, recommender systems, smart cities, Internet of things, digital and mobile health, human-robot interaction, social network analysis, data science for social good, and other applications.
The large volume and variety of data, being generated at an accelerated velocity, creates an important opportunity and challenge for humans and organizations. Indeed, data has become the lifeblood of today’s knowledge-driven economy and society. Although labels and annotations are becoming increasingly available to support supervised machine learning, the vast majority of the data remains either unlabeled or not reliably labeled, thus continuing the critical demand for unsupervised machine learning which can automatically discover knowledge and structure that is hidden in the data with or without human supervision. Unfortunately, conventional unsupervised learning techniques, especially clustering, face tremendous challenges when mining Big Data due to several known challenges, including one or more of the following: high complexity, heterogeneity/variety, large volumes, high throughputs/rapid generation, temporal, geospatial, and distributed cloud-based data-driven applications. In addition to the previous technical challenges, the increasing reliance on Big Data is making an unprecedented impact on society, raising a plethora of contemporary challenges associated with Big Data such as privacy concerns, algorithmic biases, and ethical considerations. 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 under significant challenges and constraints.
The goal of this book is to provide a coverage of recent developments in Big Data clustering methods, tools, frameworks, and applications.
Topics of interest include, but are not limited to:
- Clustering large scale data
- Clustering heterogeneous data
- Modern 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
- 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 multimodal Data
- Semi-supervised clustering
- 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 for distributed Big Data frameworks such as Hadoop and Spark
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/svmono.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, Knowledge Discovery & Web Mining Lab, Dept. of Computer Engineering & Computer Science, University of Louisville, Louisville, USA
- Dr. Chiheb-Eddine Ben N’Cir, LARODEC Laboratory, University of Tunis, ESEN, University of Mannouba, Tunisia