Special Issue "Information-Theoretical Methods in Data Mining"
Deadline for manuscript submissions: 30 April 2019
Prof. Kenji Yamanishi
Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Japan
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Interests: information-theoretic learning theory; data mining; knowledge discovery; data science; big data analysis; machine learning; minimum description length principle; model selection; anomaly detection; change detection; health care data analysis; glaucoma progression prediction
Data mining is a rapidly growing field with the aim of analyzing big data in academia and industry. In it information-theoretical methods play a key role in discovering useful knowledge from a large amount of data. For example, probabilistic modeling of data sources based on information-theoretical methods such as maximum entropy, the minimum description length principle, rate-distortion theory, Kolmogorov complexity, etc. have turned to be very effective in machine learning problems in data mining such as model selection, regression, clustering, classification, structural/relational learning, association/causality analysis, transfer learning, change/anomaly detection, stream data mining, sparse modeling, etc. As real data become complex, further advanced information-theoretical methods are currently emerging to adapt data in realistic sources such as non-i.i.d. sources, heterogeneous sources, network type data sources, sparse sources, etc. Information-theoretical data mining methods have successfully been applied to a wide range of application areas including finance, education, marketing, intelligent transportation systems, multi-media processing, health care, network science, etc.
This special issue specifically emphasizes research that addresses data mining problems using information-theoretical methods. It includes research on a novel development of information-theoretical methods for specific applications to data mining, and a new data mining problem using information theory. Submissions at the boundaries of information theory, data mining, and other related areas such as machine learning, network science, etc. are also welcome.
Prof. Kenji Yamanishi
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 papers will be 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 1600 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.
- Data Mining
- Knowledge Discovery
- Data Science
- Machine Learning
- Big Data
- Information Theory
- Minimum Description Length Principle
- Source Coding
- Probabilistic Modeling
- Latent Variable Modeling
- Network Science