Information-Theoretic Methods in Data Analytics, 2nd Edition

Dear Colleagues,

Information-theoretic methods in data analytics are an important and basic research tool to solve practical problems under uncertain situations. They serve as a fundamental building block in modern data mining, machine learning, pattern recognition, and deep learning, among other fields, as well as in classical data modeling, This research direction has received consistent attention from both academia and industry. Despite the necessity and success of information-based methods, the research community to share information-based paradigms and their applications.

This Special Issue aims to collect works on novel information-driven methods and their applications—hopefully with an emphasis on statistical frameworks and flows—in numerous domains, such as medicine, finance, business, biology, marketing, education, etc. Works that include topics such as information, entropy, statistical inference, data compression, feature selection and extraction, discovery of clusters and/or communities in association with prediction, outlier detection, association rule mining, recommendation systems, reinforcement learning, pattern recognition, deep neural networks, and other data-based statistical and analytical topics are of particular interest.

Dr. Kichun Lee
Guest Editor

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