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 This Special Issue provides a platform for researchers to present their novel and unpublished works in the domain of granular computing and data mining.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	25.3 Days	CHF 1800
Entropy entropy	2.1	4.9	1999	22.3 Days	CHF 2600
Information information	2.4	6.9	2010	16.4 Days	CHF 1600
Mathematical and Computational Applications mca	1.9	-	1996	25.4 Days	CHF 1400
Mathematics mathematics	2.3	4.0	2013	18.3 Days	CHF 2600

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Published Papers (4 papers)

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All BDCC Entropy Information MCA Mathematics

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29 pages, 11619 KiB  

Open AccessArticle

MSA-GCN: Multistage Spatio-Temporal Aggregation Graph Convolutional Networks for Traffic Flow Prediction

by Ji Feng, Jiashuang Huang, Chang Guo and Zhenquan Shi

Mathematics 2024, 12(21), 3338; https://doi.org/10.3390/math12213338 - 24 Oct 2024

Viewed by 849

Abstract

Timely and accurate traffic flow prediction is crucial for stabilizing road conditions, reducing environmental pollution, and mitigating economic losses. While current graph convolution methods have achieved certain results, they do not fully leverage the true advantages of graph convolution. There is still room [...] Read more.

Timely and accurate traffic flow prediction is crucial for stabilizing road conditions, reducing environmental pollution, and mitigating economic losses. While current graph convolution methods have achieved certain results, they do not fully leverage the true advantages of graph convolution. There is still room for improvement in simultaneously addressing multi-graph convolution, optimizing graphs, and simulating road conditions. Based on this, this paper proposes MSA-GCN: Multistage Spatio-Temporal Aggregation Graph Convolutional Networks for Traffic Flow Prediction. This method overcomes the aforementioned issues by dividing the process into different stages and achieves promising prediction results. In the first stage, we construct a latent similarity adjacency matrix and address the randomness interference features in similarity features through two optimizations using the proposed ConvGRU Attention Layer (CGAL module) and the Causal Similarity Capture Module (CSC module), which includes Granger causality tests. In the second stage, we mine the potential correlation between roads using the Correlation Completion Module (CC module) to create a global correlation adjacency matrix as a complement for potential correlations. In the third stage, we utilize the proposed Auto-LRU autoencoder to pre-train various weather features, encoding them into the model’s prediction process to enhance its ability to simulate the real world and improve interpretability. Finally, in the fourth stage, we fuse these features and use a Bidirectional Gated Recurrent Unit (BiGRU) to model time dependencies, outputting the prediction results through a linear layer. Our model demonstrates a performance improvement of 29.33%, 27.03%, and 23.07% on three real-world datasets (PEMSD8, LOSLOOP, and SZAREA) compared to advanced baseline methods, and various ablation experiments validate the effectiveness of each stage and module. Full article

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

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17 pages, 367 KiB  

Open AccessArticle

Three-Valued Concept Analysis for 2R Formal Contexts

by Taisheng Zeng, Huilai Zhi, Yinan Li, Daxin Zhu and Jianbing Xiahou

Mathematics 2024, 12(19), 3015; https://doi.org/10.3390/math12193015 - 27 Sep 2024

Viewed by 516

Abstract

Russian Roulette is a well-known cruel gambling game and its concepts and methods have been exploited in a lot of research fields for decades. However, abundant useful information contained in the process of Russian Roulette is seldom studied with a mathematical model with [...] Read more.

Russian Roulette is a well-known cruel gambling game and its concepts and methods have been exploited in a lot of research fields for decades. However, abundant useful information contained in the process of Russian Roulette is seldom studied with a mathematical model with interpretability. To this end, we define the $2R$ formal context to model Russian Roulette and carry out 3-valued concept analysis for $2R$ formal contexts to mine useful information. At first, the uniqueness of $2R$ formal contexts is discussed from a formal concept analysis viewpoint. And then we propose 3-valued $2R$ concepts and discuss their properties and the connections with the basic $2R$ concepts. Experimental analysis demonstrates that 3-valued $2R$ concept lattices can show many more different details compared with basic $2R$ concept lattices. Finally, a case study about a Chinese herbal medicine is introduced to demonstrate the feasibility of the proposed model. Full article

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

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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

Viewed by 1502

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

Cited by 1 | Viewed by 958

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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