Machine Learning For Big Data Analysis Siddhartha Bhattacharyya Editor Hrishikesh Bhaumik Editor Anirban Mukherjee Editor Sourav De Editor by Siddhartha Bhattacharyya (editor); Hrishikesh Bhaumik (editor); Anirban Mukherjee (editor); Sourav De (editor) 9783110551433, 3110551438 instant download after payment.

This volume comprises six well-versed contributed chapters devoted to report the latest fi ndings on the applications of machine learning for big data analytics. Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. The possible challenges in this direction include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy.

Big data analytics is the process of examining large and varied data sets - i.e., big data - to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions. This volume is intended to be used as a reference by undergraduate and post graduate students of the disciplines of computer science, electronics and telecommunication, information science and electrical engineering.

THE SERIES: FRONTIERS IN COMPUTATIONAL INTELLIGENCE

The series Frontiers In Computational Intelligence is envisioned to provide comprehensive coverage and understanding of cutting edge research in computational intelligence. It intends to augment the scholarly discourse on all topics relating to the advances in artifi cial life and machine learning in the form of metaheuristics, approximate reasoning, and robotics. Latest research fi ndings are coupled with applications to varied domains of engineering and computer sciences. This field is steadily growing especially with the advent of novel machine learning algorithms being applied to different domains of engineering and technology. The series brings together leading researchers that intend to continue to advance the fi eld and create a broad knowledge about the most recent research.

• Well organized chapters discuss most recent trends of hybrid metaheuristic techniques.


• Most efficient approach especially for industrial research.


• Each chapter with case studies and examples.