Advances in Data Mining and Coding Theory for Data Compression
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".
Deadline for manuscript submissions: 28 February 2025 | Viewed by 30
Special Issue Editors
2. Faculty of Electrical Engineering and Computer Science, University of Maribor, SI-2000 Maribor, Slovenia
Interests: knowledge discovery; data mining; clustering; community detection; complex networks; data compression
Special Issues, Collections and Topics in MDPI journals
Interests: tree growth simulation; parallel computation; remote sensing; evolutionary computation; computer graphics; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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
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. Entropy 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 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.
Keywords
- big data
- network data
- data mining
- learning
- clustering
- community detection
- data compression
- machine learning
- information science
- coding theory
