Information-Theoretic Data Mining
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 39236
Special Issue Editor
Special Issue Information
Dear Colleagues,
Predictions are difficult, especially those about the future. Data mining is the process of converting data to knowledge using methods at the crossroads of machine learning, artificial intelligence, statistics, and database systems. Data science and data engineering help create knowledge in various forms of models, which in turn are used to finding anomalies, patterns, and correlations within large amounts of data to help predict the future or explain the present.
Currently, data mining is implemented on large scales to help solve business and societal problems. Information-theoretic data mining plays a special role due to its solid foundations in information theory. The field is slowly maturing with contributions from many fields of information and computer science and related sciences. This interdisciplinary characteristic leads to different viewpoints, different implementations, and different approaches.
Therefore, contributions are being solicited to this Special Issue on the many faces of information theory in data mining, presenting both theoretical and practical aspects of developing and using data mining approaches.
Prof. Boštjan Brumen
Guest Editor
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Keywords
- data mining
- data engineering
- big data
- data science
- machine learning
- artificial intelligence
- knowledge extraction
- information theory
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