21st IEEE International Conference on Data Mining Workshops, ICDMW 2021

IEEE Computer Society, 2021.

Academic editor: B. Xue, M. Pechenizkiy, S. K. Yun, P. Miettinen, J. Bailey, X. Wu

Editor-in-chief: L. O’Conner, H. Torres

The 21th IEEE International Conference on Data Mining (IEEE ICDM 2021) is a premier and
truly international conference for researchers and practitioners in the broad area of data mining.
The ICDM Workshops program (IEEE ICDMW) aims to provide a platform for multiple
workshops with a range of more focused topics to be discussed and explored, where attendees can
present their original results, exchange research ideas, identify limitations, and explore new
opportunities on the theoretical development and real-world applications of data mining techniques.

Due to the global COVID-19 pandemic, IEEE ICDMW was held on virtually on 7th December
2021, followed by the IEEE ICDM 2021 conference. This year, we receive 24 proposals. After the
workshop proposal review, paper review and merging of workshops, the final IEEE ICDMW 2021
program consisted of 18 workshops, including 6 full-day and 12 half-day workshops. Overall,
IEEE ICDMW received 266 papers while 131 papers (i.e. 50%) being presented in the final
program. All the papers have been through a peer-review process to ensure high-quality papers
being presented in the ICDMW 2021 proceedings.

A wide range of research topics in data mining have been included in IEEE ICDMW 2021,
covering both theoretical research and real-world applications. Among the more traditional areas
of data mining, ICDMW 2021 includes recommendation systems, information retrieval,
information extraction, natural text, high-dimensional data mining, multi-source data, incremental
learning, continual learning, sentiment analysis, deep learning, clustering, concept drift, novelty
detection, feature engineering, time-series data mining, and data optimization. Furthermore,
emerging research areas include data mining for bioinformatics, healthcare, engineering service,
anomaly analytics, utility-driven data mining and learning, data in legal domain, spatial and spatiotemporal
data mining, evolutionary computation based data mining, and cybersecurity.

 

Chapters

Deep Reinforcement Learning Task for Portfolio Construction

Belyakov B., Sizykh D., , in: 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021.: IEEE Computer Society, 2021. P. 1077–1082.

Added: February 4, 2022

Keywords: data mining, programming, machine learning, data modelling, machine learning and data mining, deep reinforcement learning