New Advances in Granular Computing and Data Mining

Topic Information

Dear Colleagues,

Data mining has actively contributed to solving many real-world problems with a variety of techniques. During the last few years, several challenges have emerged, such as the occurrence of imbalanced data, multi-label and multi-instance problems, low quality, and noisy data. Granular computing provides a powerful tool for multiple granularity and multiple-view data analysis at different granularity levels, which has demonstrated strong capabilities and advantages in intelligent data analysis, pattern recognition, machine learning and uncertain reasoning. Based on granular computing, many new methods have been developed in order to solve the problem of big data analytics and mining. This Special Issue provides a platform for researchers to present their novel and unpublished works in the domain of granular computing and data mining. We are pleased to invite you, along with the members of your research group, to contribute to the forthcoming MDPI Special Issue, entitled “New Advances in Granular Computing and Data mining”. Potential topics include, but are not limited to, the following:

1. Rough set-based data mining;
2. Fuzzy set-based data mining;
3. Knowledge-based granular data mining;
4. Knowledge-based three-way data analytics;
5. Machine learning;
6. Three-way decision;
7. Three-way clustering;
8. Uncertainty analysis;
9. Cognitive computing;
10. Features selection.

Prof. Dr. Xibei Yang
Prof. Dr. Bin Xie
Dr. Pingxin Wang
Dr. Hengrong Ju
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Big Data and Cognitive Computing BDCC	3.7	4.9	2017	18.2 Days	CHF 1800	Submit
Entropy entropy	2.7	4.7	1999	20.8 Days	CHF 2600	Submit
Information information	3.1	5.8	2010	18 Days	CHF 1600	Submit
Mathematical and Computational Applications mca	1.9	-	1996	22.5 Days	CHF 1400	Submit
Mathematics mathematics	2.4	3.5	2013	16.9 Days	CHF 2600	Submit

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Published Papers (1 paper)