Future Trends and Challenges in Data Mining Technology

Topic Information

Dear Colleagues,

Regarding future trends and challenges in data mining, first of all, the development of data mining technology will benefit from the continuous development of machine learning, deep learning, and artificial intelligence technologies. With the development of these technologies, data mining algorithms will become more complex and efficient, becoming better suited to solving practical problems. Secondly, with the development of the Internet and big data technology, the amount of data is constantly growing. This will create more opportunities for data mining, but it will also bring about more challenges. Data mining algorithms will require higher performance and better scalability to adapt to big data environments. Thirdly, with the development of data mining technology, privacy issues have gradually become the focus of attention. Data mining algorithms need to ensure data security and privacy to protect users' private information. Finally, with the development of data mining technology, algorithm interpretability will become an important research direction. Data mining algorithms need to be easier to understand so that users can better understand and interpret the results.

Prof. Dr. Qun Dai
Dr. Qing Tian
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400
Data data	2.2	4.3	2016	26.8 Days	CHF 1600
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400
Information information	2.4	6.9	2010	16.4 Days	CHF 1600
Mathematics mathematics	2.3	4.0	2013	18.3 Days	CHF 2600

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25 pages, 14600 KiB  

Open AccessArticle

Using Visualization to Evaluate the Performance of Algorithms for Multivariate Time Series Classification

by Edgar Acuña and Roxana Aparicio

Data 2025, 10(5), 58; https://doi.org/10.3390/data10050058 - 24 Apr 2025

Abstract

In this paper, we use visualization tools to give insight into the performance of six classifiers on multivariate time series data. Five of these classifiers are deep learning models, while the Rocket classifier represents a non-deep learning approach. Our comparison is conducted across [...] Read more.

In this paper, we use visualization tools to give insight into the performance of six classifiers on multivariate time series data. Five of these classifiers are deep learning models, while the Rocket classifier represents a non-deep learning approach. Our comparison is conducted across fifteen datasets from the UEA repository. Additionally, we apply data engineering techniques to each dataset, allowing us to assess classifier performance concerning the available features and channels within the time series. The results of our experiments indicate that the ROCKET classifier consistently achieves strong performance across most datasets, while the Transformer model underperforms, likely due to the limited number of instances per class in certain datasets. Full article

(This article belongs to the Topic Future Trends and Challenges in Data Mining Technology)

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