Grey Clustering Research Articles

Grey models for data analysis and decision-making in uncertainty during pandemics

We analyse in this study quite a few uncertain decision-making situations concomitant to a pandemic spread and some of the models using grey theory to deal with such situations. We present four stylised models to tackle different decision support situations at the time of any pandemic crisis, such as COVID-19. Eight diverse problems or situations of risk mitigation and decision-making under uncertainties are proposed in this research and the methodologies employing grey incidence analysis, grey clustering, grey prediction, and grey programming models are offered. Numerical illustrations of the applications of these methodologies are also typified in this paper for future development. A practical application of the widely used grey prediction model is also demonstrated in this study to predict the number of COVID-19 infections and fatalities of five Indian states during a considered period of study. The implications of the study are for data scientists, decision-makers, and practitioners to use the benefits of grey theory in dealing with various situations of uncertainty, such as a spread of a pandemic.

History of Peripartum Depression Moderates the Association Between Estradiol Polygenic Risk Scores and Basal Ganglia Volumes in Major Depressive Disorder

The neurobiological differences between women who have experienced a peripartum episode and those who have only had episodes outside of this period are not well understood. 64 parous female patients with major depressive disorder that have either a positive (n=30) or negative (n=34) history of peripartum depression (PPD) underwent MRI acquisition to obtain structural brain images. An independent two-sample t-test comparing patients with and without a history of PPD was performed using voxel-based morphometry analysis (VBM). Additionally, polygenic risk scores (PRSs) for estradiol were calculated and a moderation analysis was conducted between 3 estradiol PRSs and PPD history status on extracted cluster volumes using IBM SPSS PROCESS macro. The VBM analysis identified larger grey matter volumes in bilateral clusters encompassing the putamen, pallidum, caudate, and thalamus in patients with PPD history compared to patients without a history. The moderation analysis identified a significant interaction of 2 estradiol PRSs and PPD history on grey matter cluster volumes with a positive effect in PPD women and a negative effect in women with no history of PPD. Our findings demonstrate that women who have experienced a peripartum episode are neurobiologically distinct from women who have no history of PPD in a cluster within the basal ganglia, an area important for motivation, decision-making, and emotional processing. Furthermore, we show that the genetic load for estradiol has a differing effect in this area based on PPD status which supports the claim that PPD is associated with sensitivity to sex steroid hormones.

Corruption prevention in organizational clustering in Indonesia: through the role of the HU-model in detecting corruption

PurposeThis paper aims to test the effectiveness of the Haryono Umar (HU)-model used in corruption prevention strategies through corruption detection as a tool for detecting corruption because the mode of corruption is increasingly dynamic and complex by focusing on the causes of corruption: pressure, opportunity, rationalization, capability and lack of integrity.Design/methodology/approachThe research uses multiple regression methods, classification and regression trees and the HU-model application system developed by researchers. The research sample uses secondary data from financial reports on the Indonesia stock exchange according to organizational clustering (such as red, grey and green areas).FindingsThe research result showed that of the 470 sample companies, there were 445 companies, or 98.9%, in the red cluster (indicated corruption), 19 companies, or 4.04, in the green clusters or not indicated corruption and six companies, or 1.28%, were included in the grey cluster or potential corruption. By knowing the cluster of an organization, efforts to prevent corruption can be made effective and efficient. Implementing the HU-model proves that the amount of pressure, the abundance of opportunities, the ease of rationalization and the high level of position and authority strengthen the drive for corruption if there is a lack of integrity.Research limitations/implicationsEach internal organization can use this model independently and find conditions related to corruption so that they can immediately take action to prevent it.Originality/valueThe application of the HU-model is a discovery in preventing corruption by focusing on the possibility of corruption occurring in each organization through organizational clustering.

GCP: A multi-strategy improved wireless sensor network model for environmental monitoring

Nowadays, smart environmental monitoring devices are widely used in various fields, and one of the most representative tools is the wireless sensor network (WSN). WSNs are easy to deploy and provide real-time information feedback, which is very suitable for environmental monitoring. As we all know, the environmental monitoring network because of the special nature of its work requirements, the need for uninterrupted and real-time transmission of monitoring data, which leads to energy consumption is extremely large, and this cannot meet the needs of its long-term work. Existing traditional routing has problems such as unscientific cluster head election and high redundancy in data transmission, which usually lead to a large amount of energy consumption, which is not conducive to the long-term stable operation of sensor networks. In this paper, we improve the traditional routing protocol and design a cluster head election method based on the genetic algorithm, which proposes a new fitness function in terms of energy, distance, and the number of nodes in the cluster, and performs the selection of cluster head nodes based on this method. In addition, we propose a new grey prediction model, which can realize the real-time update of data queues, and optimize the data transmission process of traditional WSNs based on this prediction model to reduce the amount of intra-cluster data transmission. Combining these improvements, a grey cluster prediction (GCP) model is proposed, and the performance of the model is tested based on real mine and soil data sets. The simulation results show that the model significantly reduces energy loss and extends the network life cycle while ensuring the integrity of data transmission. It can also meet the requirements of long-term stable operation of environmental monitoring equipment.

Analysis of Rock Mechanical Characteristics and Establishment of an Evaluation System for Coal Measures in the Longtan Formation, Guizhou Province.

The mechanical properties of coal measure rocks and their evaluation significantly impact the process and efficacy of coal measure exploration and development. This study focuses on the Guizhou Longtan Formation coal measure. The mechanical and fracturing characteristics of coal measure rock samples are analyzed via well coring, geophysical logging, and indoor experiments. Additionally, predictive models for rock mechanical parameters are developed, and an evaluation system for the Longtan Formation coal measure rock mass is established. The findings are as follows: (1) Coal measure rocks in Guizhou's Longtan Formation exhibit a relatively low elastic modulus and tensile strength, but a substantial variation in compressive strength. The triaxial compressive strength, elastic modulus, and residual strength increase nonlinearly with increasing confining pressure. As the confining pressure increases, the failure mode of the mudstone and siltstone transitions from primarily splitting failure to shearing failure. (2) Strong correlations are calculated between logging parameters and rock mechanical parameters and are used to construct three regression prediction models, yielding an average prediction accuracy of approximately 85% for rock mechanical properties. (3) Considering the rock mechanical properties, rock mass structure and stratigraphic characteristics, and the occurrence environment related to the characteristics of rock mass affecting coal measure gas development, eight evaluation indices are selected. The analytic hierarchy process-entropy weighting method is used to determine the weights of the comprehensive evaluation indices, and a coal measure rock mass evaluation system is established by utilizing gray clustering analysis. The evaluation results categorize the mudstone group (mudstone and silty mudstone) as Classes III-IV, the fine sandstone group as Classes I and II, and the siltstone group (muddy siltstone and siltstone) as Classes II and III. A comparative analysis with fuzzy comprehensive evaluation results and extenics theory evaluation results demonstrated a high level of consistency. These findings benefit coal measure rock mechanics classification and quantitative research on rock mechanics properties, providing a solid foundation for efficient coal measure gas exploration and development.

Synergizing a Deep Learning and Enhanced Graph-Partitioning Algorithm for Accurate Individual Rubber Tree-Crown Segmentation from Unmanned Aerial Vehicle Light-Detection and Ranging Data

The precise acquisition of phenotypic parameters for individual trees in plantation forests is important for forest management and resource exploration. The use of Light-Detection and Ranging (LiDAR) technology mounted on Unmanned Aerial Vehicles (UAVs) has become a critical method for forest resource monitoring. Achieving the accurate segmentation of individual tree crowns (ITCs) from UAV LiDAR data remains a significant technical challenge, especially in broad-leaved plantations such as rubber plantations. In this study, we designed an individual tree segmentation framework applicable to dense rubber plantations with complex canopy structures. First, the feature extraction module of PointNet++ was enhanced to precisely extract understory branches. Then, a graph-based segmentation algorithm focusing on the extracted branch and trunk points was designed to segment the point cloud of the rubber plantation. During the segmentation process, a directed acyclic graph is constructed using components generated through grey image clustering in the forest. The edge weights in this graph are determined according to scores calculated using the topologies and heights of the components. Subsequently, ITC segmentation is performed by trimming the edges of the graph to obtain multiple subgraphs representing individual trees. Four different plots were selected to validate the effectiveness of our method, and the widths obtained from our segmented ITCs were compared with the field measurement. As results, the improved PointNet++ achieved an average recall of 94.6% for tree trunk detection, along with an average precision of 96.2%. The accuracy of tree-crown segmentation in the four plots achieved maximal and minimal R2 values of 98.2% and 92.5%, respectively. Further comparative analysis revealed that our method outperforms traditional methods in terms of segmentation accuracy, even in rubber plantations characterized by dense canopies with indistinct boundaries. Thus, our algorithm exhibits great potential for the accurate segmentation of rubber trees, facilitating the acquisition of structural information critical to rubber plantation management.

A cooperative detection scheme for malicious nodes based on D-S trust evidence reasoning in mobile crowdsensing networks

Mobile crowdsensing possesses excellent collaborative capabilities and has become an important solution for addressing complex sensing tasks. However, the high mobility and anonymity of nodes bring security risks such as forging false data and maliciously consuming platform resources. To address these problems, a cooperative detection scheme for malicious nodes based on Dempster-Shafer(D-S) trust evidence reasoning is proposed. Firstly, the sensing platform collects trust data of nodes through a trust evidence chain, which includes data credibility, recommendation trustworthiness, task cooperativeness and behavioral credibility. Meanwhile, trust decay and control mechanisms are adopted to dynamically update the trust evidence chain. Additionally, a recommendation trust network based on recommendation value is constructed through the collaboration between platform and nodes. The Isolation Forest is used to analyze node behavior, and the behavioral credibility is calculated using Bayes’ rule. Then, K-Means, Gaussian Mixture Model (GMM), Three-way Decision and Grey Cluster are used to classify nodes into trusted, suspicious and malicious sets. Finally, the purification of suspicious set is achieved through raft consensus among trusted nodes. Simulation tests demonstrate the performance of the proposed scheme in accuracy, false negative rate, false positive rate and time consumption. Overall, the scheme has better detection performance when using GMM.

Retraction Note: Gray relational clustering model for intelligent guided monitoring horizontal wells

Retraction Note: Gray relational clustering model for intelligent guided monitoring horizontal wells

A Novel Grey Fuzzy Clustering Model and its Application on Students’ Performance Evaluation

Aiming to address the issues of traditional student evaluation methods, which tend to be overly subjective and overlook the intrinsic data structure, this paper introduces a novel grey fuzzy clustering model named the grey entropy game-weighted fuzzy c-means (GEG-WFCM) model. Firstly, subjective weights are calculated using the subjective-objective relationship analysis method, while objective weights are determined through the entropy weight method. Then, a comprehensive approach is adopted, leveraging game theory to calculate the final weights. Based on these comprehensive weights, the relative grey correlation coefficient and fuzzy weighted c-mean algorithm are incorporated to yield the ultimate evaluation results. The proposed model was applied to evaluate student performance, and the experiments show that it can obtain scientific and reasonable results. The model not only acknowledges the expertise of experts but also respects the objectivity of the data, thus circumventing the limitations of purely subjective judgments, and surpassing traditional evaluation methods.

Circadian rapid eye movement sleep expression is associated with brain microstructural integrity in older adults

Rapid eye movement sleep (REMS) is increasingly suggested as a discriminant sleep state for subtle signs of age-related neurodegeneration. While REMS expression is under strong circadian control and circadian dysregulation increases with age, the association between brain aging and circadian REMS regulation has not yet been assessed. Here, we measure the circadian amplitude of REMS through a 40-h in-lab multiple nap protocol in controlled laboratory conditions, and brain microstructural integrity with quantitative multi-parameter mapping (MPM) imaging in 86 older individuals. We show that reduced circadian REMS amplitude is related to lower magnetization transfer saturation (MTsat), longitudinal relaxation rate (R1) and effective transverse relaxation rate (R2*) values in several white matter regions mostly located around the lateral ventricles, and with lower R1 values in grey matter clusters encompassing the hippocampus, parahippocampus, thalamus and hypothalamus. Our results further highlight the importance of considering circadian regulation for understanding the association between sleep and brain structure in older individuals.

Explainable rumor detection based on grey clustering: Fusion of manual features and deep learning features

The importance of rumor detection on social media is self-evident. However, many existing studies have focused on exploring potential features in text content and propagation patterns, while neglecting a key aspect—the explainability of the model. The comment content can provide support for the credibility of the detection. Nevertheless, most studies that use comments encode them into specific models, rarely considering their semantic attitudes and standpoints, making it difficult for models to explain why a post is a rumor. In this study, we propose an Explainable rumor detection model based on Grey clustering called MDE-Grey, which combines Manual features and Deep learning features. In terms of manual features, we constructed a relevant vocabulary based on the specific comment environment of rumors to capture comment standpoints. In terms of deep learning features, we have designed a GCN sub network that includes two attention mechanisms to capture noteworthy content in posts and comments. Finally, we constructed a new grey clustering model to fuse the two types of features and obtain the final prediction. In the grey clustering model, we designed new whitening functions to capture the intrinsic relationship between features and rumor categories, ensuring the traceability of prediction results. The experiments on three datasets and case studies have demonstrated the effectiveness of the MDE-Grey model in detecting rumors and explaining the results.

Portfolio management under capital market frictions: a grey clustering approach

International portfolio management is influenced by the existence of “frictions”, factors or events that interfere with trade, which are linked in financial literature to market-specific factors, such as available information, restrictions, investor protection, or market liquidity. Given the wide variety of factors that can be included in these categories, scientific studies typically focus on a reduced number of indicators at a time in order to offer an in depth analysis of their impact. We offer a consolidated view of the perspectives observed in financial literature by proposing a novel index for market frictions that includes all these four components and rank fifteen post-communist East European capital markets based on their index values. We then constructed various scenarios by assuming different levels of importance for the criteria used in index construction. By employing grey clustering analysis, we cluster these capital markets into three categories—strongly recommended, recommended with some reserve, and not recommended—based on the importance given by the decision maker to these factors. The results show that some of the studied markets are in the same cluster, irrespective of the chosen scenario. The only market always included in the “strongly recommended” category is Hungary, indicating that it is a good investment option for international participants. Bulgaria and Slovakia are always regarded as “recommended with reserve” markets, whereas the Republic of Moldova is part of the “not recommended” category. The other markets show a degree of variability that can be explained by different investor perspectives. This study contributes to the existing literature by combining the advantages of grey clustering and portfolio analysis. Investors can use this approach during the decision-making process related to their investments.

What kind of urban brand ecology attracts talent best? Grey configuration analysis of 98 Chinese cities

PurposeTalent is a key resource for urban development, and building and disseminating urban brands have an important impact on attracting talent. This paper explores what kind of urban brand ecology (UBE) can effectively enhance urban talent attraction (UTA). We explore this question using a novel grey quantitative configuration analysis (GQCA) model.Design/methodology/approachTo develop the GQCA model, grey clustering is combined with qualitative configuration analysis (QCA). We conducted comparative configuration analysis of UTA using fuzzy set QCA (fsQCA) and the proposed GQCA.FindingsWe find that the empirical results of fsQCA may contradict the facts, and that the proposed GQCA effectively solves this problem.Practical implicationsBased on the theory of UBE, we identify bottleneck factors for improving UTA at different stages. Seven configuration paths are described for cities to enhance UTA. Theoretically, this study expands the application boundaries of UBE.Originality/valueThe proposed GQCA effectively solves the problem of inconsistent analysis and facts caused by the use of a binary threshold by the fsQCA. In practical case studies, the GQCA significantly improves the reliability of configuration comparisons and the sensitivity of QCA to cases, demonstrating excellent research performance.

Dominant Gray Level-based Genetic K-means Clustering Algorithm for MRI Image Segmentation

Dominant Gray Level-based Genetic K-means Clustering Algorithm for MRI Image Segmentation

A group decision-making method for technology selection of petrochemical enterprises

Selecting appropriate energy conservation and emission reduction technologies is essential for the sustainable developments of petrochemical enterprises. It usually involves experienced experts from multi-domain and thus can be regarded as a large-scale group decision-making problem. Existing methods assumed that different experts use the same linguistic term set following the same semantics to express their evaluation values, and derived the ranking of alternatives by an indirectly obtained group preference matrix. Such an approach may induce information loss. To avoid these defects, this study introduces a model based on rank centrality and expert differences to choose appropriate technologies for petrochemical enterprises. First, an attitude parameter is introduced into a linguistic scale function to unify different judgment benchmarks of experts regarding the same linguistic term. Next, a direct weight elicitation method based on rank centrality is developed to deduce the individual rank vector of alternatives. A grey clustering method based on the similarities of individual rank vectors is extended to cluster experts into small groups, and the silhouette coefficient is used to determine clusters weights. Afterwards, the rank vectors of alternatives corresponding to subgroups and the global rank vector are calculated by the weighted averaging operator. Alternatives are ranked by the rank vector of the global group. Finally, four technologies were evaluated by the model, and the preferred technology was identified. The findings indicate that the proposed technology selection model alleviates information loss, elevates group consensus level, and provides a perspective for petrochemical enterprises in search of energy conservation and emission reduction technologies.

Catalytic ozonation of 2, 4-dichlorophenoxyacetic acid wastewater by Fe-La@ZE catalyst

This study developed a salt-tolerant multimetal ozone catalyst Fe-La@Zeolite (Fe-La@ZE) and effectively catalyzed the ozone depletion of high-salinity 2,4-dichlorophenoxyacetic acid wastewater (2,4-D) and an entropy to evaluate the comprehensive gray cluster analysis model was created using catalytic ozonation conditions. Characterization results show that Fe-La@ZE has a developed pore structure and Fe and La are successfully loaded into the interior and surface of the zeolite support, which are in the form of α-Fe2O3 and La2O3. The COD removal rate of zeolite adsorption, ozone oxidation alone and catalyzed ozone oxidation with empty zeolite support are 4.9 %, 46.5 % and 57.5 %. The removal rates of COD, total phenol and 2,4-dichlorophenol by catalytic ozonation under the conditions of pH = 0.89, ozone dose of 0.2 g/L, catalyst filling rate of 10 % and height to diameter ratio of the reactor 6:1 are 84.2 %, 87.8 % and 99.0 % respectively. Hydroxy radicals (OH) and superoxide radicals (O2) are the main active substances that catalyze the degradation of 2,4-dichlorophenol in the ozone oxidation system. Water quality characterization shows that most phenolic organic compounds and macromolecular organic acids are broken down and converted into intermediate products. The comprehensive analysis model of entropy gray clustering is used to evaluate the operating condition data of the catalytic ozonation treatment of 2,4-dichlorophenoxyacetic acid wastewater with the Fe-La@ZE catalyst and determine the optimal operating condition.

A maximum satisfaction consensus-based large-scale group decision-making in social network considering limited compromise behavior

With the enrichment of social platforms, large-scale group decision-making (LSGDM) in social networks has gradually taken shape. In practice, experts may show limited compromise behavior during consensus reaching process (CRP), which may lead to bias or even failure in decision-making results. To tackle this issue, considering limited compromise behavior, this paper proposes a maximum satisfaction consensus (MSC) model, and focusing on LSGDM with fuzzy preference relation in social networks. Firstly, an expert weight determination algorithm integrating the interactive weights and individual weights is constructed. The interactive weights are determined by social influence strength, while the individual weights are measured by the compromise degree of limited compromise behavior and the consistency level of preferences. Subsequently, a grey clustering method is developed to classify experts into subgroups, considering both opinions similarity and limited compromise behavior similarity. In CRP, considering the limited compromise behavior, an MSC model under the limited budget determined by the minimum cost consensus (MCC) model is proposed. Based on MSC and MCC models, satisfaction-cost ratio is defined to measure the efficiency of CRP. Finally, a case study of selecting of shared bicycle operators and further discussion are conducted to verify the feasibility and superiority of the study.

Prediction of industrial electricity consumption based on grey cluster weighted Markov model

Accurate prediction of industrial electricity consumption is not only beneficial to maintaining the steady development of the economy but also to conserving energy. To improve the prediction accuracy of industrial electricity consumption, a grey cluster weighted Markov model is proposed. It is applied to predict the industrial electricity consumption in four different regions in China. The prediction results are compared with the traditional discrete grey prediction model, which shows that the present model is more effective in these aspects of prediction accuracy, stability and extensibility. The research can provide theoretical references for the “West-East electricity transmission project” in China.

Two-stage consensus reaching process in social network large group decision-making with application to battery supplier selection

Trust relationships play an important role in social network large group decision making (SN-LGDM). Extant studies overlook the self-adjustment stage of decision makers (DMs) and the binding force of subgroup on preference adjustment in consensus reaching process (CRP). This article proposes a new two-stage CRP for SN-LGDM considering self-adjustment and binding force of subgroup. A new trust propagation approach is presented based on individual trust level. An improved grey clustering algorithm is devised based on the fusion of preference similarity and trust relationship. The preference stability and the awareness of big picture are defined to determine the weights of DMs. The subgroup size, cohesion and influence are integrated to determine the weights of subgroups comprehensively. For the new two-stage CRP, the first stage is self-adjustment stage. DMs may proactively choose to adjust the preference or update the trust relationship according to the degree of matching between the trust relationship and preference difference. In the second stage, four scenarios are identified and the corresponding adjustment strategies are given considering the intention of DM to modify and the binding force of subgroup, which makes the consensus more consistent with reality. A battery supplier selection case is offered to illustrate the proposed method.

Towards healthy cities: Modeling restorative potential of urban environments by coupling LiDAR-derived 3D metrics with panorama-based online survey

With the growing concern for the psychological well-being of urban dwellers, there has been a recognized need to explore the role of green environments in facilitating mental restorative benefits from the demands of city life. However, a significant gap exists in quantitatively modeling the restorative potential of urban environments, especially in terms of comprehensive 3D characteristics beyond mere greenery. This research gap has hindered our understanding of how to plan and design urban spaces that promote mental health. To bridge this gap, we conducted a study using Singapore as a case study to model the restorative potential for landscape planning and design by integrating LiDAR-derived 3D metrics, outcomes of an online survey, and a machine learning-based regularized regression model. We first collected data from 100 scenes, including both residential neighborhoods and urban parks, by extracting point cloud information from LiDAR scans for calculating a diverse set of 41 3D spatial metrics. Alongside these objective measurements, we conducted an online survey (N = 1500) using the Perceived Restorativeness Scale (PRS) where respondents evaluated the restorative potential of these scenes presented as interactive panoramic images. We subsequently utilized a regularized regression technique based on machine learning by conducting K-fold cross-validation (K = 10) with 1251 tests to identify the feature metrics significantly contributing to restorative potential. Our findings highlighted the importance of the green volumetric ratio as the most significant positive factor for restorative potential. Additionally, the results on the metrics of grey horizontal diversity and grey cluster number indicated a positive impact of unregulated and unaggregated grey components on restorative potential. The results on green horizontal diversity suggested that a uniform and concentrated spatial arrangement of greenery is a positive predictor, while other factors like front-background ratio, colorfulness, and slope variation also positively influenced restorative potential. In essence, our study not only provides novel insights but also introduces innovative methods for quantitatively assessing and characterizing the quality of urban environments that enhance psychological well-being. These findings inform the practical planning and design strategies aimed at fostering healthy and sustainable urban spaces.