Clustered Data Research Articles

Molecular transmission network analysis of newly diagnosed HIV-1 infections in Nanjing from 2019 to 2021

ObjectiveThe objective of this study was to conduct a comprehensive analysis of the molecular transmission networks and transmitted drug resistance (TDR) patterns among individuals newly diagnosed with HIV-1 in Nanjing.MethodsPlasma samples were collected from newly diagnosed HIV patients in Nanjing between 2019 and 2021. The HIV pol gene was amplified, and the resulting sequences were utilized for determining TDR, identifying viral subtypes, and constructing molecular transmission network. Logistic regression analyses were employed to investigate the epidemiological characteristics associated with molecular transmission clusters.ResultsA total of 1161 HIV pol sequences were successfully extracted from newly diagnosed individuals, each accompanied by reliable epidemiologic information. The analysis revealed the presence of multiple HIV-1 subtypes, with CRF 07_BC (40.57%) and CRF01_AE (38.42%) being the most prevalent. Additionally, six other subtypes and unique recombinant forms (URFs) were identified. The prevalence of TDR among the newly diagnosed cases was 7.84% during the study period. Employing a genetic distance threshold of 1.50%, the construction of the molecular transmission network resulted in the identification of 137 clusters, encompassing 613 nodes, which accounted for approximately 52.80% of the cases. Multivariate analysis indicated that individuals within these clusters were more likely to be aged ≥ 60, unemployed, baseline CD4 cell count ≥ 200 cells/mm3, and infected with the CRF119_0107 (P < 0.05). Furthermore, the analysis of larger clusters revealed that individuals aged ≥ 60, peasants, those without TDR, and individuals infected with the CRF119_0107 were more likely to be part of these clusters.ConclusionsThis study revealed the high risk of local HIV transmission and high TDR prevalence in Nanjing, especially the rapid spread of CRF119_0107. It is crucial to implement targeted interventions for the molecular transmission clusters identified in this study to effectively control the HIV epidemic.

NipalsMCIA: Flexible Multi-Block Dimensionality Reduction in R via Non-linear Iterative Partial Least Squares.

With the increased reliance on multi-omics data for bulk and single cell analyses, the availability of robust approaches to perform unsupervised analysis for clustering, visualization, and feature selection is imperative. Joint dimensionality reduction methods can be applied to multi-omics datasets to derive a global sample embedding analogous to single-omic techniques such as Principal Components Analysis (PCA). Multiple co-inertia analysis (MCIA) is a method for joint dimensionality reduction that maximizes the covariance between block- and global-level embeddings. Current implementations for MCIA are not optimized for large datasets such such as those arising from single cell studies, and lack capabilities with respect to embedding new data. We introduce nipalsMCIA, an MCIA implementation that solves the objective function using an extension to Non-linear Iterative Partial Least Squares (NIPALS), and shows significant speed-up over earlier implementations that rely on eigendecompositions for single cell multi-omics data. It also removes the dependence on an eigendecomposition for calculating the variance explained, and allows users to perform out-of-sample embedding for new data. nipalsMCIA provides users with a variety of pre-processing and parameter options, as well as ease of functionality for down-stream analysis of single-omic and global-embedding factors. nipalsMCIA is available as a BioConductor package at https://bioconductor.org/packages/release/bioc/html/nipalsMCIA.html, and includes detailed documentation and application vignettes. Supplementary Materials are available online.

COVID-19 and human resource management: status, trends and research directions

PurposeThis study aims to conduct an extensive bibliometric analysis of research across COVID-19 and human resource management (HRM). It captures an exhaustive conceptual understanding of theoretical foundations, research trends, developments and research directions in the HRM domain.Design/methodology/approachA set of 505 HRM and COVID-19-specific articles collected from the Scopus database were systematically analyzed using a two-tier method. In the first tier of analysis, the evolution and current state of research are identified using citation analysis. In the second tier, network analysis and content analysis of research clusters and thematic mapping are done to identify the prominent research themes and research gaps and suggest future research directions.FindingsThe study highlights the emergence of six research clusters: SHRM and competitive advantage, employer branding and employee engagement, crisis management and resilience, challenges, career shock and job demand resources and burnout. The thematic mapping categorizes the themes into four categories: motor, basic, emerging or declining, and niche research themes published on COVID-19 and HRM. To understand the socio-cultural dynamics and cross-cultural issues during human resource management, the findings emphasized the need for the increased contribution of researchers and practitioners, especially from the developing and emerging nation’s context. Increased co-authorship among influential authors and institutions will also help formulate strategies and policies to effectively deal with similar pandemics.Originality/valueUnlike the previous literature review, the present findings provide meaningful insights for formulating people management techniques, policies, and practices in response to COVID-19 or similar pandemics.

The Potential for Digital Phenotyping in Understanding Mindfulness App Engagement Patterns: A Pilot Study.

Background: Low app engagement is a central barrier to digital mental health efficacy. With mindfulness-based mental health apps growing in popularity, there is a need for new understanding of factors influencing engagement. This study utilized digital phenotyping to understand real-time patterns of engagement around app-based mindfulness. Different engagement metrics are presented that measure both the total number of app-based activities participants completed each week, as well as the proportion of days that participants engaged with the app each week. Method: Data were derived from two iterations of a four-week study exploring app engagement in college students (n = 169). This secondary analysis investigated the relationships between general and mindfulness-based app engagement with passive data metrics (sleep duration, home time, and screen duration) at a weekly level, as well as the relationship between demographics and engagement. Additional clinically focused analysis was performed on three case studies of participants with high mindfulness activity completion. Results: Demographic variables such as gender, race/ethnicity, and age lacked a significant association with mindfulness app-based engagement. Passive data variables such as sleep and screen duration were significant predictors for different metrics of general and mindfulness-based app engagement at a weekly level. There was a significant interaction effect for screen duration between the number of mindfulness activities completed and whether or not the participant received a mindfulness notification. K-means clusters analyses using passive data features to predict mindfulness activity completion had low performance. Conclusions: While there are no simple solutions to predicting engagement with mindfulness apps, utilizing digital phenotyping approaches at a population and personal level offers new potential. The signal from digital phenotyping warrants more investigation; even small increases in engagement with mindfulness apps may have a tremendous impact given their already high prevalence of engagement, availability, and potential to engage patients across demographics.

First report of PST-producing Microseira wollei from China reveals its novel toxin profile

Microseira wollei, a globally distributed freshwater bloom-forming benthic cyanobacterium, is known for its production of cyanotoxins and taste and odor (T&O). While CYN (Cylindrospermopsin)-producing populations of M. wollei are confined to Australia, PST (Paralytic shellfish toxins)-producing populations have been exclusively documented in North America. In this study, four benthic cyanobacterial strains, isolated from West Lake in China, were identified as M. wollei based on morphological and phylogenetic analyses. Detection of sxtA gene and UPLC-MS/MS analysis conclusively confirmed the PST-producing capability of M. wollei CHAB5998. In the phylogenetic tree of 16S rDNA, M. wollei strains formed a monophyletic group with two subclades. Notably, non-PST-producing Chinese strains clustered with Australian strains in Clade II, while all other strains, including PST-producing ones, clustered in Clade I. Additionally, CHAB5998 contains ten PST variants, of which STX, NEO, GTX2, GTX3, GTX5 and C1 were identified for the first time in M. wollei. Sequence analysis of PST biosynthetic gene cluster (sxt) genes indicated potential base variations, gene rearrangements, insertions, and deletions in the strain CHAB5998. Also, sxt gene has a longer evolutionary history in M. wollei than that in cyanobacteria from Nostocales. Multiple recombination breakpoints detected in sxt genes and the inconsistency in the topology of the phylogenetic trees between sxt and 16S rDNA suggested that multiple horizontal gene transfers (HGT) have occurred. Overall, the present study marks the first documented occurrence of PST-producing M. wollei outside of North America and identifies it as the first toxic freshwater benthic cyanobacterium in China. This revelation implies that benthic cyanobacteria may pose a higher environmental risk in China than previously acknowledged.

Nexus Between Sarcopenia and Microbiome Research: A Bibliometric Exploration.

Despite over 30 years of microbiome and skeletal muscle research, no quantitative analysis of sarcopenia and the microbiome literature had been conducted. Our bibliometric study examined research status, hotspots, and future trends. We utilized bibliometric techniques to search the Science Citation Index Extended Database on February 27, 2023, using the Bibliometrix package in R to create a map displaying scientific production and subject categories. Collaborative network maps between countries/regions were visualized using Scimago Graphica, while VOSviewer explored collaboration modes among individuals and institutions. We analyzed the top 25 emerging keywords, top co-occurring keyword networks, and co-occurring keyword clusters using CiteSpace. A total of 997 articles were retrieved for sarcopenia and microbiome, of which 633 papers were analyzed. Both the number of publications and total citation frequency had been continuously increasing. The United States had the highest total citation frequency, while China had the highest number of publications. Research on the impact of the microbiome on sarcopenia was in its nascent stage and spans multiple disciplines, including nutrition, microbiology, geriatrics, immunology, endocrinology and metabolism, molecular biology, and sports medicine. The University of Copenhagen contributed the most to the number of publications (n=16), with Tibbett M (n=7) and Hulver MW (n=7) among the top authors. The most published journal was "Nutrients" (n=24). Analysis of keywords and clusters revealed new research hotspots in microbes and sarcopenia, such as malnutrition, dietary fiber, signaling pathways, frailty, and intestinal permeability. Research on the impact of the microbiome on sarcopenia is in its infancy and spans multiple disciplines. Malnutrition, dietary fiber, signaling pathways, frailty, and intestinal microbes are currently research hotspots. Furthermore, the visual atlas analysis of research on microbes and sarcopenia helps to track the knowledge structure in research fields related to sarcopenia and microbes, providing direction for future research.

As treated analyses of cluster randomized trials

As treated analyses of cluster randomized trials

Efficient Classes of Robust Ratio Type Estimators of Mean and Variance in Adaptive Cluster Sampling

This paper proposes two classes of robust ratio type estimators of finite population mean and two classes of robust ratio type estimators of finite population variance using a single auxiliary variable under the adaptive cluster sampling design. Seven robust ratio type estimators have been developed from each class. The generalized expressions of bias and mean square error for each class have been derived up to the first order of approximation. The exact expressions of bias and MSE for all the developed estimators have been presented. Using simulation studies conducted in R programming, the high efficiency of all the developed estimators over similar existing estimators in adaptive cluster sampling has been demonstrated. . KEYWORDS :Adaptive cluster sampling, Classes of robust ratio type estimators, Hidden clustered data, Estimation of mean, Estimation of variance.

The effect of number of clusters and magnitude of within-cluster homogeneity in outcomes on the performance of four variance estimators for a marginal multivariable Cox regression model fit to clustered data in the context of observational research.

Researchers often estimate the association between the hazard of a time-to-event outcome and the characteristics of individuals and the clusters in which individuals are nested. Lin and Wei's robust variance estimator is often used with a Cox regression model fit to clustered data. Recently, alternative variance estimators have been proposed: the Fay-Graubard estimator, the Kauermann-Carroll estimator, and the Mancl-DeRouen estimator. Using Monte Carlo simulations, we found that, when fitting a marginal Cox regression model with both individual-level and cluster-level covariates: (i) in the presence of weak to moderate within-cluster homogeneity of outcomes, the Lin-Wei variance estimator can result in estimates of the SE with moderate bias when the number of clusters is fewer than 20-30, while in the presence of strong within-cluster homogeneity, it can result in biased estimation even when the number of clusters is as large as 100; (ii) when the number of clusters was less than approximately 20, the Fay-Graubard variance estimator tended to result in estimates of SE with the lowest bias; (iii) when the number of clusters exceeded approximately 20, the Mancl-DeRouen estimator tended to result in estimated standard errors with the lowest bias; (iv) the Mancl-DeRouen estimator used with a t-distribution tended to result in 95% confidence that had the best performance of the estimators; (v) when the magnitude of within-cluster homogeneity in outcomes was strong or very strong, all methods resulted in confidence intervals with lower than advertised coverage rates even when the number of clusters was very large.

Prediction of the Signaling Pathway in Polycystic Ovary Syndrome Using an Integrated Bioinformatics Approach.

Objectives The purpose of this study was to define the underlying biological mechanisms of PCOS utilizing the protein-protein interaction networks that were constructed based on the putative disease-causing genes for PCOS. Design No animals were used in this research because this study is an In-Silico studies which mainly uses softwares and online analysis tools. Participants/Materials, Settings Genes datasets related with PCOS were obtained from Genecard. Methods The protein-protein interaction networks (PPIN) of PCOS were created using the String Database after genes related with PCOS were obtained from Genecard. After that, we performed an analysis of the hub-gene clusters extracted from the PPIN using the ShinyGO algorithm. In the final step of this research project, functional enrichment analysis was used to investigate the primary biological activities and signaling pathways that were associated with the hub clusters. Results The Genecard database provided the source for the identification of a total of 1072 potential genes related with PCOS. The PPIN that was generated by using the genes that we collected above contained a total of 82 genes and three different types of cluster interaction interactions. In addition, after conducting research on the PPIN with the shinyGO plug-in, 19 of the most important gene clusters were discovered. The primary biological functions that were enriched in the key clusters that were developed were ovarian stereoidogenesis, breast cancer pathway, regulation of lipid and glucose metabolism by AMPK pathway, and ovarian stereoidogenesis. The integrated analysis that was performed in the current study demonstrated that these hub clusters and their connected genes are closely associated to the pathogenesis of PCOS. Limitations Several of the significant genes that were identified in this study, such as ACVR1, SMAD5, BMP6, SMAD3, SMAD4 and AMH. It is necessary to do additional research using large samples, several centers, and multiple ethnicities in order to verify these findings. Conclusions The integrated analysis that was performed in the current study demonstrated that these hub clusters and their connected genes are closely associated to the pathogenesis of PCOS. This information may possibly bring unique insights for the treatment of PCOS as well as the investigation of its underlying pathogenic mechanism.

Maintaining the validity of inference from linear mixed models in stepped-wedge cluster randomized trials under misspecified random-effects structures.

Linear mixed models are commonly used in analyzing stepped-wedge cluster randomized trials. A key consideration for analyzing a stepped-wedge cluster randomized trial is accounting for the potentially complex correlation structure, which can be achieved by specifying random-effects. The simplest random effects structure is random intercept but more complex structures such as random cluster-by-period, discrete-time decay, and more recently, the random intervention structure, have been proposed. Specifying appropriate random effects in practice can be challenging: assuming more complex correlation structures may be reasonable but they are vulnerable to computational challenges. To circumvent these challenges, robust variance estimators may be applied to linear mixed models to provide consistent estimators of standard errors of fixed effect parameters in the presence of random-effects misspecification. However, there has been no empirical investigation of robust variance estimators for stepped-wedge cluster randomized trials. In this article, we review six robust variance estimators (both standard and small-sample bias-corrected robust variance estimators) that are available for linear mixed models in R, and then describe a comprehensive simulation study to examine the performance of these robust variance estimators for stepped-wedge cluster randomized trials with a continuous outcome under different data generators. For each data generator, we investigate whether the use of a robust variance estimator with either the random intercept model or the random cluster-by-period model is sufficient to provide valid statistical inference for fixed effect parameters, when these working models are subject to random-effect misspecification. Our results indicate that the random intercept and random cluster-by-period models with robust variance estimators performed adequately. The CR3 robust variance estimator (approximate jackknife) estimator, coupled with the number of clusters minus two degrees of freedom correction, consistently gave the best coverage results, but could be slightly conservative when the number of clusters was below 16. We summarize the implications of our results for the linear mixed model analysis of stepped-wedge cluster randomized trials and offer some practical recommendations on the choice of the analytic model.

Subgroup detection in linear growth curve models with generalized linear mixed model (GLMM) trees.

Growth curve models are popular tools for studying the development of a response variable within subjects over time. Heterogeneity between subjects is common in such models, and researchers are typically interested in explaining or predicting this heterogeneity. We show how generalized linear mixed-effects model (GLMM) trees can be used to identify subgroups with different trajectories in linear growth curve models. Originally developed for clustered cross-sectional data, GLMM trees are extended here to longitudinal data. The resulting extended GLMM trees are directly applicable to growth curve models as an important special case. In simulated and real-world data, we assess performance of the extensions and compare against other partitioning methods for growth curve models. Extended GLMM trees perform more accurately than the original algorithm and LongCART, and similarly accurate compared to structural equation model (SEM) trees. In addition, GLMM trees allow for modeling both discrete and continuous time series, are less sensitive to (mis-)specification of the random-effects structure and are much faster to compute.

Single-cell transcriptome reveals the heterogeneity of malignant ductal cells and the prognostic value of REG4 and SPINK1 in primary pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related deaths, with very limited therapeutic options available. This study aims to comprehensively depict the heterogeneity and identify prognostic targets for PDAC with single-cell RNA sequencing (scRNA-seq) analysis. ScRNA-seq analysis was performed on 16 primary PDAC and three adjacent lesions. A series of analytical methods were applied for analysis in cell clustering, gene profiling, lineage trajectory analysis and cell-to-cell interactions. In vitro experiments including colony formation, wound healing and sphere formation assay were performed to assess the role of makers. A total of 32,480 cells were clustered into six major populations, among which the ductal cell cluster expressing high copy number variants (CNVs) was defined as malignant cells. Malignant cells were further subtyped into five subgroups which exhibited specific features in immunologic and metabolic activities. Pseudotime trajectory analysis indicated that components of various oncogenic pathways were differentially expressed along tumor progression. Furthermore, intensive substantial crosstalk between ductal cells and stromal cells was identified. Finally, genes (REG4 and SPINK1) screened out of differentially expressed genes (DEGs) were upregulated in PDAC cell lines. Silencing either of them significantly impaired proliferation, invasion, migration and stemness of PDAC cells. Our findings offer a valuable resource for deciphering the heterogeneity of malignant ductal cells in PDAC. REG4 and SPINK1 are expected to be promising targets for PDAC therapy.

Entrepreneurial Resilience and Adaptation in Times of Crisis: A Bibliometric Study on Business Continuity in SMEs

This bibliometric analysis investigates the evolving landscape of research on small and medium-sized enterprises (SMEs) focusing on entrepreneurial resilience and adaptation strategies in times of crisis. By utilizing VOSviewer for thematic clusterization and trend analysis, the study maps out the interrelationships among key research themes and tracks their evolution from 2019 to 2022. The analysis identifies foundational themes such as crisis management and performance, alongside emergent themes like digital transformation and gender issues. Furthermore, the research highlights underexplored areas, including the impacts of natural disasters and the role of employees in organizational resilience, presenting potential avenues for future scholarly work. Additionally, an examination of author collaboration networks reveals robust partnerships and suggests areas for potential interdisciplinary research.

Geospatial Distribution of Prenatally and Postnatally Diagnosed Congenital Heart Disease: Implications for Equitable Care from a Fetal Heart Society Research Collaborative Study

To characterize patterns in the geospatial distribution of pre- and postnatally diagnosed congenital heart disease (CHD) across 6 surgical centers. A retrospective, multicenter case series from the Fetal Heart Society identified patients at 6 centers from 2012 through 2016 with prenatally (PrND) or postnatally (PoND) diagnosed hypoplastic left heart syndrome (HLHS) or d-transposition of the great arteries (TGA). Geospatial analysis for clustering was done by the average nearest neighbor (ANN) tool or optimized hot spot tool, depending on spatial unit and data type. Both point location and county case rate per 10 000 live births were assessed for geographic clustering or dispersion. Of the 453 CHD cases, 26% were PoND (n=117), and 74% were PrND (n=336). PrND cases, in all but one center, displayed significant geographic clustering by the ANN. Conversely, PoND cases tended toward geographic dispersion. Dispersion of PoND HLHS occurred in 2 centers (ANN=1.59, P<.001; and 1.47, P=.016), and PoND TGA occurred in 2 centers (ANN=1.22, P<.05; and ANN=1.73, P<.001). Hot spot analysis of all CHD cases (TGA and HLHS combined) revealed clustering near areas of high population density and the tertiary surgical center. Hot spot analysis of county-level case rate, accounting for population density, found variable clustering patterns. Geographic dispersion among postnatally detected CHD highlights the need for a wider reach of prenatal cardiac diagnosis tailored to the specific needs of a community. Geospatial analysis can support centers in improving the equitable delivery of prenatal care.

Young children’s performance on a design fluency task: longitudinal data on total number of designs, clustering and switching, and regression-based norms

ABSTRACT Introduction Design fluency (DF) tasks are commonly used to assess executive functions such as attentional control, cognitive flexibility, self-monitoring and strategy use. Next to the total number of correct designs, the standard outcome of a DF task, clustering and switching can help disentangle the processes underlying DF performance. We present the first longitudinal study of 4–8-year-old children’s developmental DF trajectories. Method At initial enrollment, children (n = 228) were aged between 4.05 and 6.88 years (M = 5.18, SD = 0.59) and attended Dutch primary schools. The DF task was administered at three time points, each time point separated by approximately 1 year. Data were analyzed using mixed regression for total number of correct designs and switching, and mixed logistic regression analysis for clustering. Results The total number of correct designs increased linearly across the three time points. Across all time points, children made very few clusters, and most clusters consisted of only 3 designs. Clustering only increased at the third assessment compared to the two previous assessments. Switching increased up to the second assessment, but not after that. The number of switches was highly correlated with the total number of correct designs at all time points (r = 0.78 to r = 0.85). These developmental trajectories were similar for all children regardless of their baseline age. Normative data are given for the total number of correct designs and switching. Conclusions Children as of age 4 onwards can perform a DF task. For children as young as 4–8 years old, computing clustering, and switching measures is of limited value to study cognitive processes underlying DF performance, next to the total number of correct designs. There were no sex differences on any of the DF outcomes. Level of parental education (LPE) was positively associated with the total number of correct designs and switching.

Factors associated with engagement in HIV care for young people living with perinatally acquired HIV in England: An exploratory observational cohort study.

Identifying which young people living with perinatally acquired HIV (PHIV) are less likely to engage in care is crucial to allow targeted interventions to support them to attend clinic. We adapted an existing Engagement in Care (EIC) algorithm for adults with HIV in England, for use in young people. We applied it to data from young people with PHIV in the Adolescents and Adults Living with Perinatal HIV (AALPHI) cohort. The algorithm predicts the timing of the next scheduled clinic visit, within 1-6 months of current visit, based on routine clinical data. Follow-up was 12-months from AALPHI baseline interview. Each person-month was classified as engaged in care or not. Logistic regression models (allowing for clustered data) were used to explore baseline characteristics associated with being engaged in care, adjusting for a priori variables (time from interview, sex, age, ethnicity, country of birth). Potential characteristics were across 7 domains: sociodemographic; risk behaviour practices; mental health; cognition; clinic setting; HIV management and experience; and HIV clinical markers. Of 316 young people, 187(59%) were female, 271(86%) of black ethnicity and 184(58%) born abroad. At baseline, median [IQR] age was 17[15-18] years, and 202(69%) had viral load ≤50 copies/ml(c/mL). 87% of 3,585 person-months were classified as engaged in care. Characteristics independently associated with poorer odds of being engaged in care were: Asian/mixed/other ethnicity, vs. black ethnicity (OR 0.44, 95% CI 0.25, 0.78, p = 0.02); ever self-harmed, vs. not (OR 0.55, 95% CI 0.32, 0.95, p = 0.03); on antiretroviral therapy (ART) and self-assessed bad/not so good adherence (OR 0.46, 95% CI 0.25, 0.84) or not on ART (OR 0.64, 95% CI 0.64, 1.21) vs. on ART and good/excellent adherence (p = 0.04)); baseline VL>50c/mL, vs VL≤50c/mL (OR 0.47, 95% CI 0.30, 0.75, p = 0.002). These characteristics can help identify individuals requiring enhanced support to maintain service engagement.

Analyzing Matched 2 × 2 Tables from all Corners

Squared 2 × 2 tables with binary data from matched pairs are typically analyzed using Cochran-Mantel-Haenszel methodology, conditional logistic regression, or random intercepts logistic regression. These are all “pair-specific” type of approaches. However, many more methods and models for clustered binary data, including marginal models and marginalizable pair-specific models, can be applied. We provide a comprehensive overview of methods and apply them all to two well-known example datasets, the prime minister’s performance and the myocardial infarction datasets. The simple setting of matched binary data allows us to compare and relate different models, methods and their estimates. A technical explanation is given for why in some settings boundary estimates are obtained. Supplementary materials for this article are available online.

A Comparative Study of Students Graduation Analysis Using Classification Methods in Undergraduate Electrical Engineering Tidar University

This research aimed to classify achievement factors for electrical engineering students at Tidar University using K-Means and Agglomerative Clustering classification algorithms. The goal was to understand if any parameters influence high-achieving student performance. The Indonesian government and private sector for university students provide significant education funds. Student scholarships are awarded based primarily on GPA and entry path, overburdening staff and causing confusion during distribution to eligible recipients. A system was needed to accommodate additional eligible criteria. The researcher selected factors to identify engineering student performance, including school origin, entry path, tuition fees, and GPA. These inputs could determine graduation status. The results compared calculation methods based on collected data accuracy, processing times, and characterizing clustered data to determine the best classification method. Agglomerative Hierarchical Clustering performed better. Accuracy testing on 600 training data points yielded 73.94% for improved K-means and 90.42% for AHC. The Average processing time was 674.92 seconds for improved K-means and 554.35 seconds for AHC. Silhouette testing also characterized calculation methods, with improved K-means scoring best at 0.654 and AHC at 0.787 using two clusters.

Multi-Objective Automatic Clustering Algorithm Based on Evolutionary Multi-Tasking Optimization

Data mining technology is the process of extracting hidden knowledge and potentially useful information from a large number of incomplete, noisy, and random practical application data. The clustering algorithm based on multi-objective evolution has obvious advantages compared with the traditional single-objective method. In order to further improve the performance of evolutionary multi-objective clustering algorithms, this paper proposes a multi-objective automatic clustering model based on evolutionary multi-task optimization. Based on the multi-objective clustering algorithm that automatically determines the value of k, evolutionary multi-task optimization is introduced to deal with multiple clustering tasks simultaneously. A set of non-dominated solutions for clustering results is obtained by concurrently optimizing the overall deviation and connectivity index. Multi-task adjacency coding based on a locus adjacency graph was designed to encode the clustered data. Additionally, an evolutionary operator based on relevance learning was designed to facilitate the evolution of individuals within the population. It also facilitates information transfer between individuals with different tasks, effectively avoiding negative transfer. Finally, the proposed algorithm was applied to both artificial datasets and UCI datasets for testing. It was then compared with traditional clustering algorithms and other multi-objective clustering algorithms. The results verify the advantages of the proposed algorithm in clustering accuracy and algorithm convergence.