Machine Learning for Big Data Analytics

Dear Colleagues,

A recent report from the McKinsey Global Institute refers to machine learning as the boosting methodology for the next frontier for innovation, competition, and productivity. As a silent technology, machine learning is used in network analysis, health, search, spam filtering, recommender systems, ad placement, credit scoring, fraud detection, stock trading, energy demand prediction, and many other applications. This special issue focuses on current machine learning techniques to deal with large volumes of heterogeneous data. We aim at providing a forum for the presentation of new algorithmic developments and applications of machine learning and data mining for big data. We welcome theoretical, applied and visionary papers pointing to next generation machine learning algorithms and techniques for big data. We especially welcome challenging real world applications of machine learning. This special issue aims to reflect the state of the art in challenges faced by machine learning developers and to present the most important and relevant advances to overcome these challenges.

We welcome papers in:

Methods and Applications of Machine Learning
Machine Learning Methods for Big Data
Analysis of Massive Graphs
Distributed Data Mining
Real-Time and Real-World Applications

Position papers and state of the art reviews are especially welcome.

Dr. João Gama
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• machine learning
• data mining
• learning from data streams
• learning from big data
• learning from networked data
• learning from graph data
• real-world applications of machine learning
• distributed data mining