Topic Editors

School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Prof. Dr. Bin Xie
College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, China
Dr. Pingxin Wang
School of Science, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Dr. Hengrong Ju
School of Information Science and Technology, Nantong University, Nantong 226019, China

New Advances in Granular Computing and Data Mining

Abstract submission deadline
30 July 2024
Manuscript submission deadline
30 October 2024
Viewed by
1175

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 This Special Issue provides a platform for researchers to present their novel and unpublished works in the domain of granular computing and data mining.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

  • granular computing
  • data mining
  • knowledge discovery
  • knowledge discovery
  • uncertainty analysis
  • fuzzy set

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Big Data and Cognitive Computing
BDCC
3.7 7.1 2017 18.2 Days CHF 1800 Submit
Entropy
entropy
2.1 4.9 1999 20.8 Days CHF 2600 Submit
Information
information
2.4 6.9 2010 18 Days CHF 1600 Submit
Mathematical and Computational Applications
mca
1.9 - 1996 22.5 Days CHF 1400 Submit
Mathematics
mathematics
2.3 4.0 2013 16.9 Days CHF 2600 Submit

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Published Papers (1 paper)

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16 pages, 966 KiB  
Article
A Granulation Strategy-Based Algorithm for Computing Strongly Connected Components in Parallel
by Huixing He, Taihua Xu, Jianjun Chen, Yun Cui and Jingjing Song
Mathematics 2024, 12(11), 1723; https://doi.org/10.3390/math12111723 - 31 May 2024
Viewed by 220
Abstract
Granular computing (GrC) is a methodology for reducing the complexity of problem solving and includes two basic aspects: granulation and granular-based computing. Strongly connected components (SCCs) are a significant subgraph structure in digraphs. In this paper, two new granulation strategies were devised to [...] Read more.
Granular computing (GrC) is a methodology for reducing the complexity of problem solving and includes two basic aspects: granulation and granular-based computing. Strongly connected components (SCCs) are a significant subgraph structure in digraphs. In this paper, two new granulation strategies were devised to improve the efficiency of computing SCCs. Firstly, four SCC correlations between the vertices were found, which can be divided into two classes. Secondly, two granulation strategies were designed based on correlations between two classes of SCCs. Thirdly, according to the characteristics of the granulation results, the parallelization of computing SCCs was realized. Finally, a parallel algorithm based on granulation strategy for computing SCCs of simple digraphs named GPSCC was proposed. Experimental results show that GPSCC performs with higher computational efficiency than algorithms. Full article
(This article belongs to the Topic New Advances in Granular Computing and Data Mining)
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