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


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:

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



All submissions should be done via EasyChair:
Original artwork and a signed copyright release form will be required for all accepted chapters. For author instructions, please visit:

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

Feel free to contact the book editors via email ( and regarding your chapter ideas.


Professor Olfa Nasraoui,
University of Louisville, Louisville, USA

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

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