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 This Special Issue provides a platform for researchers to present their novel and unpublished works in the domain of granular computing and data mining.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	7.1	2017	18 Days	CHF 1800	Submit
Entropy entropy	2.1	4.9	1999	22.4 Days	CHF 2600	Submit
Information information	2.4	6.9	2010	14.9 Days	CHF 1600	Submit
Mathematical and Computational Applications mca	1.9	-	1996	28.8 Days	CHF 1400	Submit
Mathematics mathematics	2.3	4.0	2013	17.1 Days	CHF 2600	Submit

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Published Papers (2 papers)

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All BDCC Entropy Information MCA Mathematics

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16 pages, 2822 KiB  

Open AccessArticle

FutureCite: Predicting Research Articles’ Impact Using Machine Learning and Text and Graph Mining Techniques

by Maha A. Thafar, Mashael M. Alsulami and Somayah Albaradei

Math. Comput. Appl. 2024, 29(4), 59; https://doi.org/10.3390/mca29040059 - 21 Jul 2024

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Abstract

The growth in academic and scientific publications has increased very rapidly. Researchers must choose a representative and significant literature for their research, which has become challenging worldwide. Usually, the paper citation number indicates this paper’s potential influence and importance. However, this standard metric [...] Read more.

The growth in academic and scientific publications has increased very rapidly. Researchers must choose a representative and significant literature for their research, which has become challenging worldwide. Usually, the paper citation number indicates this paper’s potential influence and importance. However, this standard metric of citation numbers is not suitable to assess the popularity and significance of recently published papers. To address this challenge, this study presents an effective prediction method called FutureCite to predict the future citation level of research articles. FutureCite integrates machine learning with text and graph mining techniques, leveraging their abilities in classification, datasets in-depth analysis, and feature extraction. FutureCite aims to predict future citation levels of research articles applying a multilabel classification approach. FutureCite can extract significant semantic features and capture the interconnection relationships found in scientific articles during feature extraction using textual content, citation networks, and metadata as feature resources. This study’s objective is to contribute to the advancement of effective approaches impacting the citation counts in scientific publications by enhancing the precision of future citations. We conducted several experiments using a comprehensive publication dataset to evaluate our method and determine the impact of using a variety of machine learning algorithms. FutureCite demonstrated its robustness and efficiency and showed promising results based on different evaluation metrics. Using the FutureCite model has significant implications for improving the researchers’ ability to determine targeted literature for their research and better understand the potential impact of research publications. Full article

(This article belongs to the Topic New Advances in Granular Computing and Data Mining)

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16 pages, 966 KiB  

Open AccessArticle

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

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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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