Big Data Analytics: Correspondence Factor Analysis, Clustering and Classification Algorithms and Applications

Dear Colleagues,

Data analysis, analytics, big data algorithms and artificial intelligence are having an enormous impact on all fields of scientific research. Clustering and classification algorithms, data analysis with Python or R, correspondence factor analysis, multiple correspondence analysis, hierarchical clustering and other statistical methods, theory and applications offer an interesting context for scientific contributions and applied research.

The present Topics of interest include data analysis methodological approaches; big data and artificial intelligence research; parallel and distributed data analysis including biomedical, medical, social sciences, humanities, education, economics, management, marketing, and computer science fields; and ethics, including applications, models, and best practices related to theory and applications. Emphasis is also placed on correspondence factor analysis, clustering and classification algorithms and applications as well as ethics issues regarding big data and artificial intelligence.

The goal of this Special Issue is to provide interested readers with a collection of papers describing recent developments in intelligent data analysis. Topics of interest include, but are not limited to:

• Theory and models;
• Clustering and classification algorithms;
• Cognitive computing;
• Computational intelligence;
• Data mining techniques, big data mining, and data analytics;
• Algorithms and simulation;
• Parallel and distributed data analysis;
• Big data analysis, algorithms of big data sets and data analysis with Python;
• Correspondence factor analysis of data sets and correspondence factor analysis of big data sets;
• Dimensional data: application of model-based clustering;
• Hierarchical clustering and hierarchical clustering of massive, high dimensional data sets;
• Multiple correspondence analysis in massive data sets;
• Big data and correspondence analysis in machine learning;
• Comparison of pattering methods;
• Multiple correspondence analysis, discriminant correspondence analysis or barycentric discriminant analysis, implicative statistical analysis: theory and applications;
• Web information analysis and complex data analysis;
• Biomedical data analysis;
• Biomedical and medical data applications;
• Deep learning;
• Visualizing data using correspondence analysis;
• Machine learning;
• Practical applications of data mining;
• Uncertainty in big data;
• Artificial intelligence algorithms;
• Artificial intelligence ethics;
• Artificial intelligence in education;
• Big data algorithms;
• Big data ethics;
• Big data in education.

Prof. Dr. Sofia Anastasiadou
Dr. Andreas Masouras
Dr. Christos Papademetriou
Dr. Stavros Souravlas
Dr. Stefanos Katsavounis
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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. Applied Sciences is an international peer-reviewed open access semimonthly 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.