Clustering Method Research Articles

Effect of an educational intervention based on health belief model on preventive behaviors against malaria in over 18-year-old Afghan immigrants living in Parsian

BackgroundMalaria disease is one of the most dangerous protozoan parasitic infections with a high mortality rate in developing countries. Malaria is a public health issue, especially in Hormozgan province, and is highly affected by foreign immigrants (Pakistani and Afghani); thus, the present study aimed to evaluate the effect of an intervention based on the health belief model (HBM) on the promotion of malaria prevention behaviors in Afghani immigrants over the age of 18. The participants resided in Persian city in Hormozgan province.MethodsThe present quasi-experimental study was conducted on 200 Afghans immigrants over 18 years of age who visited four comprehensive health service centers in Parsian city, south of Iran in June until December 2023. Sampling was by cluster method. In this way, the health centers were considered as clusters, and then 4 centers were randomly selected from among them (two centers of the control group and two centers of the intervention group) and participants were selected by a systematic random method by list of records in the National Integrated Health Record System (called SIB) (100participants control group, 100 participants intervention group). The data were collected using a researcher-made questionnaire based on the HBM before and after the educational intervention An educational program was designed and implemented to promote preventive behaviors against malaria in five sessions using different strategies and based on the HBM for the intervention group. The data were analyzed using independent-samples T-test, paired-samples T-test, Pearson’s correlation coefficient, analysis of covariance and linear regression. All statistical analyses and hypothesis testing were done in IBM SPSS version 25, at a significance level of 0.05.ResultsIn the intervention group, there was a significant difference in the mean scores of knowledge (6.48, 95% CI: 5.9,7.05), perceived susceptibility (10.57, 95% CI: 10.03, 11.1), perceived severity (16.61, 95% CI: 15.83, 16.83), perceived self-efficacy (18.26, 95% CI: 17.55, 18.96), perceived benefits (15.43, 95% CI: 14.68, 16.17), perceived barriers (-22.49, 95% CI: -23.63, -21.30), cues to action (15.06, 95% CI: 14.36, 15.75), and preventive behaviors (20.05, 95% CI: 19.44, 20.65), before and after the educational intervention. P-value < 0.001. The regression analysis showed that the constructs of perceived susceptibility (T = 4.72, P < 0.001), cues to action (T = 5.30, P < 0.001)and perceived self-efficacy (T = 4.93, P < 0.001) led to the greatest change in malaria prevention behaviors(R-Square = 0.549).ConclusionThe present findings showed that the HBM -based intervention was effective in preventive behaviors against malaria in Afghans. It is recommended to design suitable educational interventions in order to increase the perceived susceptibility, cues to action and self-efficacy in order to improve preventive behaviors against malaria in Afghans.

Contributions of Urban Woodlands to Bird Diversity and Abundance in The Anatolian Side of Istanbul

Aim of study: The study aimed to assess the impact of urban woodlands on bird species diversity and abundance in the Anatolian side of Istanbul, focusing on species typically absent from urban centers but found within urban environments. The findings are intended to contribute to the development of future conservation and habitat management strategies. Area of study: The study was conducted in five areas in Üsküdar and Beykoz, including an urban center, three urban woodlands (Fethipaşa, Hidiv, and Beykoz Abrahampaşa) and a natural area in Istanbul's northern region. Material and methods: Data on bird species and their abundances were collected and tabulated through 900 point counts from April 2022 to March 2023. The data was analyzed using abundance values, species richness and diversity, identifying distribution and evenness through rank abundance distribution and curves. Similarity analysis was performed using the clustering method and species composition was compared using a Venn diagram. Main results: Seventy-two bird species were identified across the study areas. A decrease in urbanization density enhances bird species richness, diversity and evenness, with the natural area recording the highest values. Urban woodlands harbor species absent from more urbanized locations. Distinctions in species distribution and bird diversity between the areas were revealed. Research highlights: Urban woodlands are crucial areas for wildlife in metropolises, providing significance from both ecological and sociocultural perspectives. Although they may not match natural areas in species diversity, these woodlands significantly contribute to bird diversity within the urban environments. Further research is needed to explore the unique characteristics of each woodland and their contributions to urban ecosystems.

Morphological Evaluation and Phenolic Content of Wild Prunus cerasifera Ehrh. Fruits from Slovenia

Wild fruit species offer significant nutritional, environmental, and economic benefits. Among them, Prunus cerasifera Ehrh. (myrobalan) stands out for its resilience and nutrient-rich fruits, traditionally consumed fresh or dried. This study aimed to assess the morphological and biochemical diversity of 21 wild P. cerasifera accessions from Slovenia, focusing on 18 fruit and stone traits, including size, shape, color, and phenolic compounds such as total monomeric anthocyanins and total phenolic content (TPC). The objective was to identify genotypes with potential for cultivation and food production or as parental components in breeding programs. The results showed variability in fruit length (2.19–2.82 cm), width (2.18–2.99 cm), weight (67.98–150.30 g), firmness, and juiciness, with notable differences in stone characteristics. Phytochemical analysis revealed a wide range of TPC (277–1756 mg/100 g) and anthocyanin levels (0–710 mg/100 g), with higher concentrations in darker fruits. Statistical analysis compared PAM and AGNES clustering methods, finding that PAM with five clusters and AGNES with two clusters provided similar insights. The study emphasized variability in morphological and chemical traits among clusters, with specific accessions showing particularly valuable traits, such as accession RK13, which had high TPC and anthocyanins and a non-adherent stone.

Derivation of outcome-dependent dietary patterns for low-income women obtained from survey data using a supervised weighted overfitted latent class analysis.

Poor diet quality is a key modifiable risk factor for hypertension and disproportionately impacts low-income women. Analyzing diet-driven hypertensive outcomes in this demographic is challenging due to the complexity of dietary data and selection bias when the data come from surveys, a main data source for understanding diet-disease relationships in understudied populations. Supervised Bayesian model-based clustering methods summarize dietary data into latent patterns that holistically capture relationships among foods and a known health outcome but do not sufficiently account for complex survey design. This leads to biased estimation and inference and lack of generalizability of the patterns. To address this, we propose a supervised weighted overfitted latent class analysis (SWOLCA) based on a Bayesian pseudo-likelihood approach that integrates sampling weights into an exposure-outcome model for discrete data. Our model adjusts for stratification, clustering, and informative sampling, and handles modifying effects via interaction terms within a Markov chain Monte Carlo Gibbs sampling algorithm. Simulation studies confirm that the SWOLCA model exhibits good performance in terms of bias, precision, and coverage. Using data from the National Health and Nutrition Examination Survey (2015-2018), we demonstrate the utility of our model by characterizing dietary patterns associated with hypertensive outcomes among low-income women in the United States.

NS-IDBSCAN: An efficient incremental clustering method for geospatial data in network space

The exponential growth of big data presents a significant task for the incremental clustering problem. This paper proposes a density-based total clustering method in network space (NS-IDBSCAN) to cluster newly generated data. Instead of re-clustering the entire massive database, which would be much larger than the new datasets, we perform clustering with only the added data. Based on the current clustering results of the old data, the proposed algorithm checks the role of each newly added point and its neighbors in performing clustering. Depending on the density, an added point can be classified as noise, border, or core. This approach dramatically reduces the response time. Moreover, using a hash table with the key as the element's index corresponding to the point Id to store the cluster Id eliminates the need for searching by direct access, further increasing processing speed. Additionally, the proposed algorithm improves accuracy by reducing the intra-cluster distance when eliminating the phenomenon that the border points may belong to more than one cluster, as presented in the discussion. The proposed algorithm is compared with the spatial data clustering algorithm in network space. Test results on three data sources downloaded from OpenStreetMap, ESRI Open Data, and Inside Airbnb demonstrate that the proposed method significantly speeds up the processing time. The accuracy of the proposed algorithm is measured using eight indicators: Silhouette, BSS, WSS, WB, Davis-Bouldin, Dunn, Calinski-Harabasz, and NMI.

Identification of RNA uridylation subtypes, and the prognostic and therapeutic value of RNA uridylation in systemic lupus erythematosus

The uridylation of 3′-RNA-a major process in epitranscriptomics- is catalyzed by terminal uridylyl transferases (TUTases), which are involved in multiple diseases and the immune response. Nonetheless, the role of TUTases in systemic lupus erythematosus (SLE) remains unknown. Here we identified increased level of MTPAP and ZCCHC6 and decreased level of PAPD5 and ZCCHC11 in SLE patients from Gene Expression Omnibus (GEO) GSE50772, GSE65391, and GSE121239. The random forest model was applied to screen 4 TUTase candidates (MTPAP, ZCCHC6, PAPD5, and ZCCHC11) to predict the susceptibility of SLE. A nomogram was constructed based on the 4 selected TUTase regulators. Decision curve analysis indicated that patients could benefit from the nomogram. Moreover, based on the 4 differentially expressed genes, individuals with SLE were divided into three patterns (Cluster A-C) using the consensus clustering method. Cluster B was enriched in adaptive immune cells, with the lowest TCE signature expression, manifesting a higher Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) than that in Cluster A and C. whereas, Cluster C was enriched in innate immune cells, with the highest T-cell exhaustion (TCE) signature expression, manifesting lower SLEDAI than that in Cluster B. Clinically, lupus nephritis (LN) patients manifested increased expression of MTPAP and ZCCHC6 and decreased expression of PAPD5 and ZCCHC11 in PBMCs using Quantitative Polymerase Chain Reaction (q-PCR). Immunohistochemistry (IHC) illustrated higher level of ZCCHC6 in the kidneys of LN patients than that in NC. In summary, TUTases could predict the occurrence of SLE and stratify patients based on their immune characteristics, eventually predicting the disease activity and guiding immune therapy.

Consensus methods with Nash and Kalai-Smorodinsky bargaining game for large-scale group decision-making

With the significant advancements in communication technology, group decision-making (GDM) can now be implemented online, allowing a large number of decision-makers (DMs) to participate concurrently. However, current methods for large-scale group decision-making (LSGDM) are primarily suitable for 20 to 50 DMs, and their effectiveness in scenarios involving thousands or even tens of thousands of participants has yet to be fully validated. Furthermore, as the number of participants increases, the evaluation information becomes increasingly diverse and complex. At the same time, the social networks associated with the DMs typically become sparse, making information sharing and consensus building more challenging. In light of these challenges, we develop two new methods based on cooperative games to effectively address the challenges in super LSGDM. First, we propose a two-stage semi-supervised fuzzy C-means (FCM) clustering method with trust constraints, which aims to address the issue of sparsity in relationships within large-scale social networks. This method utilizes trust relationships as reliable resources and prior knowledge to guide and supervise the clustering process. On this basis, we discuss three scenarios from the perspective of cooperative games: (i) subgroup optimal consensus adjustments in non-cooperative situations, (ii) group optimal consensus adjustments in cooperative situations, and (iii) subgroup optimal consensus adjustments in cooperative situations. Subsequently, we view the consensus adjustment allocation as a cost cooperative game problem and propose two new LSGDM consensus methods based on Nash Bargaining (NB) and Kalai-Smorodinsky Bargaining (KSB). Finally, experiments on real datasets demonstrate the superiority and reliability of our proposed LSGDM methods.

The Impact of Digital Transformation on the Anti-Corruption and Cyber-Fraud System

The article deals with the assessment of the digital transformation impact on the anti-corruption and anti-cyber fraud system. The article explores the interconnection between three key indices: the Corruption Perceptions Index (CPI), the National Cybersecurity Index (NCSI), and the Information and Communication Technology (ICT) Development Index in 138 countries as of 2023. In the first stage, a preliminary analysis of the indices was conducted, which showed significant variation between countries. The average CPI value was 47.5, indicating an average level of corruption globally, but some countries have very low and very high scores. Similarly, the NCSI and ICT indices also vary, indicating uneven development of cybersecurity and information and communication technologies. Correlation analysis revealed a medium to strong positive correlation between the indices. The strongest correlation (0.61) was observed between CPI and ICT, confirming that developing digital technologies can help reduce corruption. A significant correlation was also found between NCSI and ICT (0.61), which emphasizes the importance of information technology development for improving cybersecurity. The clustering method allowed us to divide countries into three groups: countries with low scores on all three indices, countries with moderate scores, and leading countries with high levels of transparency, cybersecurity, and digital development. The study confirmed the importance of investing in digital infrastructure to increase transparency and security and pointed to the need for reforms in countries that score low on all indices.

CLUSTER ANALYSIS OF OBESITY RISK LEVELS USING K-MEANS AND DBSCAN METHODS

Obesity is defined as excessive fat accumulation and abnormal accumulation of adipose tissue in the human body that poses health risks. The causes of obesity are multifactorial and include environmental and individual factors. Several factors that cause obesity include genetic, behavioral and environmental factors. Obesity causes various problems in various fields, including health, employment, demographics, economics and family. The problem of obesity has a significant impact on public health. Therefore, understanding and predicting the level of obesity risk is important in efforts to prevent and treat obesity risk. Data on eating habits, physical activity, and other factors associated with obesity levels in certain populations can provide an important basis for understanding obesity risk. This research clusters the risk of obesity to find hidden patterns in the data. The stages in this research consist of pre-processing, clustering, and analysis. The clustering methods used are K-means and DBSCAN. In clustering using the K-means method with a parameter value of k , results are obtained with the same pattern as clustering using the DBSCAN method with a parameter value of epsilon and a minimum sample . In clustering using the K-means method with a parameter value of k , Four clusters were formed which had different patterns. The clustering results obtained in this research can be used as an effort to prevent and treat the risk of obesity.

Characteristics of Oak (Quercus petraea (Matt.) Liebl), hornbeam (Carpinus betulus L.) and ash (Fraxinus excelsior L.) in a plain-low hills-mountains-plateau sequence

The climate has changed significantly over the past few decades around the globe. To make meaningful comparisons between changes occurring over time in the tree world, comprehensive databases on the most important species and their evolution must be created. It is essential to consider latitude, longitude, altitude and local relief when analyzing data. Given the extensive distribution of oak, hornbeam and ash, a sequence analysis was conducted to definitively examine the characteristics of these species in West Plain, West Hills, Apuseni Mountains and Transylvania's Tableland. The data for all the regions comprised a total of 708511 ha, which were subjected to rigorous analysis to determine characteristics such as yield class, volume, increment, structure and participation percentage. The data was analyzed using the most appropriate modern statistical programs, including correlation matrices, ANOVA, principal component analysis, matrix data analysis and the cluster method. In relation to the altitudinal variation, oak is present at average altitudes of 200-250 m in West Plain, 400-500 m in West Hills and Transylvania's Tableland, and 900-950 m in Apuseni Mountains. Hornbeam has a similar spreading with oak, but records its presence in slightly higher altitudes of West Hills (400-700 m) and lower in Apuseni Mountains (700-750 m). Volume is also an important indicator, as it is susceptible to climate change. Oak has the highest age-volume, over 95 years, while ash and hornbeam have it less than 90 years. The results are crucial for understanding the ecological requirements of oak, hornbeam and ash, as well as for effectively managing these forests.

Deep Contrast Clustering Analysis to Distinguish Diabetic Complications in Elderly Chinese Patients.

To establish an innovative clustering method for predicting variable categories of diabetic complications in Chinese ≥65 with diabetes. We selected and extracted data from elderly patients with diabetes (n=4980) from a medical examination group of 51,400 people followed up annually from 2014 to date in Kunshan, China. A deep contrast clustering approach was used to cluster and predict diabetic complications. The clustering approach was further validated using data from elderly patients with diabetes (n=397) from one medical examination cohort of 20,000 people followed up yearly from 2014 to date in Beijing Jiuhua Hospital. The patients were clustered into 6 categories by analysing 20 indicators. Cluster 1-Heavy smoking and a high cardiovascular disease (CVD) risk; Cluster 2-High alcohol consumption, high aminotransferase levels, the highest risk of stroke complications, and a high fatty liver disease (FLD) risk; Cluster 3-High blood lipid levels and a risk of FLD and stroke complications; Cluster 4-Good health indicators and a low risk of FLD, stroke, and CVD complications; Cluster 5-Older age, higher uric acid concentration and creatinine level, and the highest risk of CVD complications; Cluster 6-Large waist circumference, high BMI, high blood pressure, and the highest risk of FLD complications. The gene for nonalcoholic fatty liver disease in cluster 2 had the highest risk coefficient. This was consistent with cluster 2, which had a higher FLD prevalence. A new clustering method was developed from two large Chinese cohorts of older patients with diabetes, which may effectively predict complications by clustering into different categories.

Significance-Based Decision Tree for Interpretable Categorical Data Clustering

Numerous clustering algorithms prioritize accuracy, but in high-risk domains, the interpretability of clustering methods is crucial as well. The inherent heterogeneity of categorical data makes it particularly challenging for users to comprehend clustering outcomes. Currently, the majority of interpretable clustering methods are tailored for numerical data and utilize decision tree models, leaving interpretable clustering for categorical data as a less explored domain. Additionally, existing interpretable clustering algorithms often depend on external, potentially non-interpretable algorithms and lack transparency in the decision-making process during tree construction. In this paper, we tackle the problem of interpretable categorical data clustering by growing a decision tree in a statistically meaningful manner. We formulate the evaluation of candidate splits as a multivariate two-sample testing problem, where a single p-value is derived by combining significance evidence from all individual categories. This approach provides a reliable and controllable method for selecting the optimal split while determining its statistical significance. Extensive experimental results on real-world data sets demonstrate that our algorithm achieves comparable performance in terms of cluster quality, running efficiency, and explainability relative to its counterparts.

An improved relative integral capacity-K-means clustering method for capacity pre-sorting of decommissioned power batteries

Abstract Capacity sorting is the primary prerequisite for the stepwise utilization of decommissioned power batteries. This study proposes an improved relative integral capacity-K-means clustering (RIC-KMC) method for preliminary sorting of decommissioned power battery capacity. Firstly, according to the electrochemical aging characteristics of decommissioned power batteries, combined with the ampere-time integration algorithm, a new lossless extraction method of the capacity characteristics of Lithium-ion batteries based on the segment voltage is proposed. Second, to minimize the interference of environmental factors and sampling errors on the charging capacity, the relative amount of charging capacity is introduced. Finally, to complete the capacity sorting before the ladder utilization, an improved RIC-KMC method is proposed, which combines the electrochemical aging feature values of decommissioned power batteries extracted from the segment voltage charging capacity with the K-means clustering algorithm. Using three sets of battery charging and discharging data to validate the sorting algorithm, the capacity sorting errors are reduced by 0.926%, 12.381%, and 10.185%, respectively, which verification the validity of the proposed RIC-KMC method. This study provides a new solution for capacity pre-sorting of decommissioned power batteries for stepwise utilization.

Machine Learning-Based Clustering Using a 12-Lead Electrocardiogram in Patients With a Implantable Cardioverter Defibrillator to Identify Future Ventricular Arrhythmia.

Implantable cardioverter defibrillators (ICDs) reduce mortality associated with ventricular arrhythmia in high-risk patients with cardiovascular disease. Machine learning (ML) approaches are promising tools in arrhythmia research; however, their application in predicting ventricular arrhythmias in patients with ICDs remains unexplored. We aimed to predict and stratify ventricular arrhythmias requiring ICD therapy using 12-lead electrocardiograms (ECGs) in patients with an ICD. This retrospective analysis included 200 adult patients who underwent ICD implantation at a single center. Patient demographics, clinical features, and 12-lead ECG data were collected. Unsupervised learning techniques, including K-means and hierarchical clustering, were used to stratify patients based on 12-lead ECG features. Dimensionality reduction methods were also used to optimize clustering accuracy. The silhouette coefficient was used to determine the optimal method and number of clusters. Of the 200 patients, 59 (29.5%) received appropriate therapy. The mean age of patients was 62.3 years, and 81.0% were male. The mean follow-up period was 2,953 days, with no significant intergroup differences. Hierarchical clustering into 3 clusters proved to be the most accurate (silhouette coefficient=0.585). Kaplan-Meier curves for these 3 clusters revealed significant differences (P=0.026). We highlight the potential of ML-based clustering using 12-lead ECGs to help in the risk stratification of ventricular arrhythmia. Future research in a larger multicenter setting may provide further insights and refine ICD indications.

Construction Method of Information Security Detection based on Clustering Algorithm

A method for K-prototype clustering, which can process mixed data types, is proposed first. An algorithm for information security evaluation of hybrid clusters based on K-prototypes was constructed. This method makes full use of the excellent global optimization performance of PSO and effectively solves the defect that the K-protection function quickly falls into local optimization. Simulation results show that the proposed method can effectively prevent local extreme values and improve overall performance.

ScDRMAE: integrating masked autoencoder with residual attention networks to leverage omics feature dependencies for accurate cell clustering.

Cell clustering is foundational for analyzing the heterogeneity of biological tissues using single-cell sequencing data. With the maturation of single-cell multi-omics sequencing technologies, we can integrate multiple omics data to perform cell clustering, thereby overcoming the limitations of insufficient information from single omics data. Existing methods for cell clustering often only consider the differences in data patterns during the analysis of multi-omics data, but the dependencies between omics features of different cell types also significantly influence cell clustering. Moreover, the high dropout rates in scRNA-seq and scATAC-seq data can impact the performance of cell clustering. We propose a cell clustering model based on a masked autoencoder, scDRMAE. Utilizing a masking mechanism, scDRMAE effectively learns the relationships between different features and imputes false zeros caused by dropout events. To differentiate the importance of various omics data in cell clustering, we dynamically adjust the weights of different omics data through an attention mechanism. Finally, we use the K-means algorithm for cluster analysis of the fused multi-omics data. On commonly used sets of 15 multi-omics datasets, our method demonstrates superior cell clustering performance on multiple metrics compared to other computational methods. In addition, when datasets exhibit varying degrees of dropout noise, our method shows better performance and stronger stability on multiple metrics compared to other methods. Moreover, by analyzing the cell clusters classified by scDRMAE, we identified several biologically significant biomarkers that have been validated, further confirming the effectiveness of scDRMAE in cell clustering from a biological perspective.

Quelling the Geometry Factor Effect in Quantum Chemical Calculations of 13C NMR Chemical Shifts with the Aid of the pecG-n (n = 1, 2) Basis Sets

A root factor for the accuracy of all quantum chemical calculations of nuclear magnetic resonance (NMR) chemical shifts is the quality of the molecular equilibrium geometry used. In turn, this quality depends largely on the basis set employed at the geometry optimization stage. This parameter represents the main subject of the present study, which is a continuation of our recent work, where new pecG-n (n = 1, 2) basis sets for the geometry optimization were introduced. A goal of this study was to compare the performance of our geometry-oriented pecG-n (n = 1, 2) basis sets against the other basis sets in massive calculations of 13C NMR shielding constants/chemical shifts in terms of their efficacy in reducing geometry factor errors. The testing was carried out with both large-sized biologically active natural products and medium-sized compounds with complicated electronic structures. The former were treated using the computation protocol based on the density functional theory (DFT) and considered in the theoretical benchmarking, while the latter were treated using the computational scheme based on the upper-hierarchy coupled cluster (CC) methods and were used in the practical benchmarking involving the comparison with experimental NMR data. Both the theoretical and practical analyses showed that the pecG-1 and pecG-2 basis sets resulted in substantially reduced geometry factor errors in the calculated 13C NMR chemical shifts/shielding constants compared to their commensurate analogs, with the pecG-2 basis set being the best of all the considered basis sets.

One-Step Multiview Fuzzy Clustering With Collaborative Learning Between Common and Specific Hidden Space Information.

Multiview data are widespread in real-world applications, and multiview clustering is a commonly used technique to effectively mine the data. Most of the existing algorithms perform multiview clustering by mining the commonly hidden space between views. Although this strategy is effective, there are two challenges that still need to be addressed to further improve the performance. First, how to design an efficient hidden space learning method so that the learned hidden spaces contain both shared and specific information of multiview data. Second, how to design an efficient mechanism to make the learned hidden space more suitable for the clustering task. In this study, a novel one-step multiview fuzzy clustering (OMFC-CS) method is proposed to address the two challenges by collaborative learning between the common and specific space information. To tackle the first challenge, we propose a mechanism to extract the common and specific information simultaneously based on matrix factorization. For the second challenge, we design a one-step learning framework to integrate the learning of common and specific spaces and the learning of fuzzy partitions. The integration is achieved in the framework by performing the two learning processes alternately and thereby yielding mutual benefit. Furthermore, the Shannon entropy strategy is introduced to obtain the optimal views weight assignment during clustering. The experimental results based on benchmark multiview datasets demonstrate that the proposed OMFC-CS outperforms many existing methods.

A clustered-LPSEIRS malware propagation model in complex networks

In this paper, we present a new malware propagation model that integrates epidemic spread, clustering, and link prediction techniques, tailored for complex network networks. Our model is based on the clustered-link prediction-susceptible-exposed-infected-recovered (clustered-LPSEIRS) epidemic model, which simulates malware dissemination within the network. Our findings reveal a significant decrease in the rate of malware spread compared to the traditional SEIR model, with this enhancement in containment attributed to the integration of clustering and link prediction methods. We also compute the basic reproduction ratio (R0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R_{0}$$\\end{document}) for our model, providing insights into the potential ramifications of malware within the network. By examining parameter variations, we enhance our understanding of the model's behavior under diverse scenarios. Additionally, we assess the influence of clustering and link prediction on mitigating malware spread, emphasizing its effectiveness in diminishing the overall impact.

INCOMPLETE multi-view clustering based on low-rank adaptive graph learning

The challenge of acquiring complete data has led to substantial progress in incomplete multi-view clustering (IMVC) methods. Because graph structures can be excellent representations of data structure relationships, exceptional performance in handling incomplete data is demonstrated by graph-based methods at present. However, these methods still have their limitations. Most incomplete multi-view algorithms primarily focus on local information, neglecting global information. Therefore, these methods cannot dynamically recover the structural relationships in incomplete data by harnessing potential information from multiple perspectives and overall structural information. In response to the aforementioned concerns, we introduced an IMVC based on low-rank adaptive graph learning (IMVC-LAGL). This method initially constructs an affinity matrix based on the inter-view adjacency relationships. It also utilizes tensor low-rank constraints and consensus representation learning to explore higher-order correlations among different views. Subsequently, it adaptively reconstructs the incomplete graph structure to ultimately obtain a complete affinity relationship. It leads to excellent clustering results by integrating relevant information within views, overall structural information and potential information from multiple perspectives. We conducted experiments comparing our algorithm with eight incomplete multi-view algorithms using five different evaluation metrics. The results show that our algorithm achieves the best clustering results across eight datasets with varying missing rates. Particularly in the BBCSport dataset and YaleB dataset, the clustering accuracy of our algorithm is improved by 19.83 % and 16.41 %, respectively, compared with the second-best algorithm, under a 50 % missing rate.