Advances in Data Mining and Coding Theory for Data Compression

Dear Colleagues,

Data mining is an important research field for revealing the structure of data, anomalies, rules, associations, clusters, and classes hidden within data sets, thereby making them understandable for further use. Data mining can be performed on structured, unstructured, and semi-structured data originating from natural, social, and artificial systems. The extracted knowledge can also be used in coding theory for more efficient data compression to encode information that requires less storage space than the original representation.

The aim of this Special Issue is to highlight the research topics of data mining and coding theory for data compression in all types of natural, artificial, social, and other complex systems. Researchers are encouraged to present the most recent developments in both theoretical and experimental studies aimed at better understanding different structured, unstructured, and semi-structured data for more efficient data compression.

Dr. Krista Rizman Žalik
Dr. Štefan Kohek
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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.