Agglomerative Hierarchical Clustering Research Articles

Sepsis subtypes and differential treatment response to vitamin C: biological sub-study of the LOVIT trial.

We hypothesised that the biological heterogeneity of sepsis may highlight sepsis subtypes with differences in response to intravenous vitamin C treatment in the Lessening Organ Dysfunction with VITamin C (LOVIT) trial. Our aims were to identify sepsis subtypes and to test whether sepsis subtypes have differences in treatment effect to vitamin C and describe putative biological effects of vitamin C treatment. We measured biomarkers of inflammation, at baseline and at 7days post-randomisation, in 457/863 (53.0%) of participants with plasma samples in the LOVIT trial. We used agglomerative hierarchical clustering on log10-transformed baseline data of 26 biomarkers to identify sepsis subtypes. We analysed differences in vitamin C treatment effect with regression models incorporating robust standard errors to report odds ratio and 95% confidence intervals (OR(95% CI)). All analyses were completed blinded to treatment allocation. Our cohort included 233/429 (54.3%) allocated to vitamin C and 224/434 (51.6%) allocated to placebo. A three-subtype model best explained the variance in our data. Subtype-2 had the highest, and subtype-3 had the lowest levels of inflammatory response. In paired longitudinal samples, vitamin C did not have discernible anti-inflammatory effects, with anti-inflammatory effects related to time since randomisation and concomitant hydrocortisone treatment. The treatment effect estimates (OR (95% CI)) for subtype-1, subtype-2 and subtype-3 were 1.04 (0.63-1.73), 1.33 (0.53-3.36) and 1.95 (0.85-4.49), respectively (test of heterogeneity p = 0.002). We report three sepsis subtypes based on inflammatory response profile. No subtype benefitted from vitamin C treatment in the LOVIT trial, with heterogeneity of treatment effect in the magnitude of harm. Funded by the Lotte and John Hecht Memorial Foundation; LOVIT ClinicalTrials.gov number,NCT03680274.

Knowledge Graphs to Accumulate and Convey Knowledge from Past Experiences in Search and Rescue Planning and Resource Allocation

ABSTRACT Planning and allocation of resources in search and rescue operations is critical and complex. Those tasks require consideration of several factors such as the nature of the emergency, location, people involved, and weather conditions. In this paper, we investigate the specific requirements for effective decision-making and planning in search and rescue operations in Norway. These requirements were determined using data gathered through expert interviews and analysis of mission reports. We propose a framework designed for search and rescue decision support, including its architecture and components. We also discuss the implementation of the service layer. We used information retrieval methods, i.e. BM25 and TF-IDF, for distance computation and compared their performance using k-means and hierarchical agglomerative clustering methods. The evaluation through silhouette score and Davies-Bouldin score shows that the hierarchical agglomerative clustering using BM25 performs well for the given dataset. The search and rescue mission reports published by the Norwegian Safety Authority were used as a dataset. In addition, the overall framework is evaluated by the domain experts using qualitative analysis. Finally, we discuss potential framework implementation challenges and provide suggestions for future research directions, in addition to presenting insights from the framework design process.

TAXONOMICAL IMPLICATIONS OF NATURAL COMPONENTS IN DELIMITATION OF SOME SPECIES OF ASTERACEAE FAMILY IN BAGHDAD

This study aimed to investigate the taxonomical relationship among eight species belonging to Asteraceae based on natural products as fresh leaves of eight species (Carthamus tinctorius L., Centaurea iberica Trevir. ex Spreng., Conyza bonariensis (L.) Cronquist, Conyza canadensis (L.) Cronquist, Launaea mucronata (Forssk.) Muschl., Senecio glaucus L., Sonchus asper (L.) Hill, and Sonchus tenerrimus L.) have been collected from different regions of Baghdad. The phytochemical screening of hexane extracts from eight plants showed different secondary metabolites in the studied species. Gas chromatography-mass spectrometry (GC-MS) analysis of eight hexane extracts showed the existence of eighty-one chemical compounds of eight taxa of Asteraceae, including hydrocarbons, alcohols, organic acids, esters, amines, and heterocyclic compounds. The agglomerative hierarchical clustering analysis dendrogram was carried out depending on 81 chemical compounds of the eight taxa of Asteraceae, gained by Euclidean distance using Ward's method. The HCA dendrogram showed the closeness of Sonchus asper and Sonchus tenerrimus and a divergence between Conyza canadensis and Conyza bonariensis and their affinity to Launaea mucronata while Carthamus tinctorius is the most divergent species from the rest of the species.

Risk factors for metabolic syndrome in the premetabolic state assessed using hierarchical clustering study in a health screening group

Early detection of a premetabolic status that is at risk for metabolic syndrome (MetS) but not meeting the criteria is crucial. This study examined 27,623 participants aged 20–50 (mean: 40.7) years who underwent initial health screening at Kangbuk Samsung Hospital (2011–2019), focusing on individuals with one or two MetS components. Hierarchical agglomerative clustering was used to form MetS risk clusters based on initial and follow-up data, including age, resting heart rate (rHR), serum uric acid (UA), C-reactive protein (CRP), gamma-glutamyl transpeptidase, and ferritin levels, and nonalcoholic fatty liver disease (NAFLD), periodontal disease, and Helicobacter pylori infection duration. Kaplan–Meier and generalized additive models were used to present the restricted mean survival time (RMST) and identify onset contributors. Clusters with NAFLD and elevated UA levels had the highest MetS risk, whereas those with uniformly low biomarker levels revealed the lowest risk. During follow-up, a cluster initially comprising 60.2% moderate-risk patients exhibited high biomarker levels and had the worst MetS prognosis (RMST: 211 days). UA, CRP levels, and rHR contributed to the incidence of MetS in the fitted model. Machine learning can predict the premetabolic state at MetS risk in a population-based cohort.

Abnormalities of resting-state EEG microstates in older adults with cognitive frailty.

This study aims to analyze the characteristics of EEG microstates across different cognitive frailty (CF) subtypes, providing insights for the prevention and early diagnosis of CF. This study included 60 eligible older adults. Their resting-state EEG microstates were analyzed using agglomerative adaptive hierarchical clustering. Microstate temporal parameters were extracted through global field power peak-based backfitting. Spearman's partial correlation analysis and linear mixed-effects models were employed to investigate the relationship between microstate temporal parameters and CF. Statistical differences were observed in transition probabilities (TPs) from microstate B to A between healthy controls (HCs) and reversible cognitive frailty (RCF) group (t = -2.076, P = 0.042). Potentially reversible cognitive frailty (PRCF) and RCF group also exhibited statistical differences in the TPs from microstate B to A (t = 3.122, P = 0.003). In the RCF group, the occurrence of microstates A and B differed significantly from microstate C (tAC = 3.455, PAC = 0.002; tBC = 3.108, PBC = 0.004). In the PRCF group, the occurrence of microstates A, B, and C differed significantly from microstate D (tAD = -3.688, PAD = 0.001; tBD = -3.334, PBD = 0.002; tCD = -4.188, PCD < 0.001). The neural networks and processing modes engaged by microstate D during executive memory tasks differ between RCF and PRCF. A decreased occurrence of microstate C and higher TPs of microstates A and B may serve as early warning signals for RCF. Conversely, an increased occurrence of microstate D and decreased TPs of microstates C and D indicate the onset of PRCF.

Vertiport Infrastructure Location Optimization for Equitable Access to Urban Air Mobility

Urban air mobility (UAM) has recently emerged as a promising new transportation mode, with various potential use cases. Facility location problems are well studied and of significant importance for various transportation modes. This work introduces a vertiport location identification framework, focusing on demand coverage and infrastructure accessibility. An Agglomerative Hierarchical Clustering (AHC) model was utilized for the identification of candidate vertiport locations, along with a k-means algorithm, for comparison and validation purposes, based on an estimated UAM demand pattern. A genetic algorithm (GA) was then formulated, for the solution of the proposed Uncapacitated and Capacitated Vertiport Location Problems (UVLP and CVLP, respectively), variations of the Uncapacitated and Capacitated Facility Location Problems. To evaluate and compare the introduced methodology, different existing facility location problems (FLPs) were considered and solved exactly using integer and linear programming. These are the Location Set Covering Problem (LSCP), the Maximal Coverage Location Problem (MCLP), and the p-median problem. The p-center problem was also considered and solved via a heuristic approach. The proposed framework is illustrated through applications in the Chicago Metropolitan Area, with the demand estimated on the basis of existing taxi and Transportation Network Company (TNC) data.

In varietate concordia - cluster analysis of EQ-5D-5L value sets in European Union countries.

The transferability of health policies in the European Union (EU) faces challenges due to economic, political, and healthcare system factors,including cross-country differences in health preferences. In order to aid policymaking,previous research has grouped EU countries based on geographical proximity or literature-based criteria, but not on health preference data. The EQ-5D-5L instrument, which measures health-related quality of life and reflects unique national health preferences shaped by cultural and social factors, is used to evaluate policies, technologies and interventions, but has not been used to group countries. Thus, this study aims to identify clusters of EU-27 countries with similar preference patterns using published EQ-5D-5L value sets. Hierarchical agglomerative clustering was employed on 13 EU-27 countries' EQ-5D-5L value sets, first analyzing regression coefficients for each dimension-level and then creating a simulated coefficient distribution considering uncertainty. Five clusters of EU-27 countries emerged: (1) Belgium, Netherlands, Sweden, Germany); (2) Hungary, Italy, Portugal; (3) Poland, Romania; (4) France, Spain; (5) Denmark, Ireland. All clusters except cluster5 gave higher importance to "Pain and Discomfort"; all clusters except cluster1 gave less importance to "Usual Activities". "Anxiety and Depression" and "Pain and Discomfort" had the largest heterogeneity in valuation across severity level. Identified clusters of countries with distinct health preferences provide insights for health policy transferability in the EU. Recognizing countries with similar health preferences can aid EU policymaking and transferability efforts, particularly in early-stage policymaking, while also fostering closer collaboration on health policy decisions and best practice sharing. Further development of national value sets within the EU is necessary for a more accurate representation.

A Cheminformatics Workflow to Select Representative TSCA Chemicals for New Approach Methodology (NAM) Screening.

The Toxic Substances Control Act (TSCA) requires the US EPA to evaluate the hazard and exposure of new and existing chemicals. New chemical notifications are typically data-poor and EPA has historically relied upon approaches including chemical categories to fill data gaps. As part of a multi-year Research Program, opportunities are being explored to leverage New Approach Methods (NAMs) in hazard and exposure assessments. Data from a battery of in vitro NAMs will be generated to form a case study for an adaptable approach to inform new chemical assessments. Herein, a cheminformatics workflow was developed to identify a set of ∼300 representative candidate chemicals for in vitro screening from the TSCA non-confidential active inventory. The freely available web application ClassyFire was used to categorize all discrete organic structures from the TSCA inventory into one of 68 primary structural categories. Large primary categories were subcategorized into smaller categories using hierarchical agglomerative clustering, ultimately yielding 180 structural terminal categories. The inventory was filtered to substances that lacked previous ToxCast bioactivity screening, were associated with physicochemical property predictions indicating non-volatile solids or liquids, and had a higher chance of procurement. Amenability predictions for liquid chromatography-mass spectrometry were also generated to provide an indication of which chemicals lent themselves to aqueous-based screening and analytical verification in solvated samples. Structures associated with transformation in solvent, potentially explosive or highly reactive, were excluded. Potential candidate substances were selected on the basis of being structurally representative of the terminal category and meeting other screenability conditions. A final set of 318 candidate chemicals were proposed to undergo analytical quality control and screening in a range of broad and targeted biological technologies for human health-relevant end points. Finally, in silico tools were applied to explore predicted hazard profiles of these candidate substances relative to the full inventory.

FunBIalign: a hierachical algorithm for functional motif discovery based on mean squared residue scores

Motif discovery is gaining increasing attention in the domain of functional data analysis. Functional motifs are typical “shapes” or “patterns” that recur multiple times in different portions of a single curve and/or in misaligned portions of multiple curves. In this paper, we define functional motifs using an additive model and we propose funBIalign for their discovery and evaluation. Inspired by clustering and biclustering techniques, funBIalign is a multi-step procedure which uses agglomerative hierarchical clustering with complete linkage and a functional distance based on mean squared residue scores to discover functional motifs, both in a single curve (e.g., time series) and in a set of curves. We assess its performance and compare it to other recent methods through extensive simulations. Moreover, we use funBIalign for discovering motifs in two real-data case studies; one on food price inflation and one on temperature changes.

Identifying accident prone areas and factors influencing the severity of crashes using machine learning and spatial analyses

Identifying accident prone areas and the contributing environmental factors can save lives and enhance infrastructure durability. This research presents an innovative use of two hierarchical clustering methods (Agglomerative Hierarchical and BIRCH clustering algorithms) to detect accident hotspots with high accident rates on the Yazd-Kerman road, in Iran. These approaches identified clusters of accidents, highlighting significant clusters and categorizing their overlap as two accident prone areas. The high percentage of overlapping results from both methods indicates a high level of consistency in the findings. Through observations, field visits, police report analysis, and interviews with locals, the primary causes of accidents in accident prone areas were identified. In one of the accident prone areas, the most significant reasons for accidents included the presence of a resting area, insufficient lighting at curves, and poor road signage, which created a dilemma zone for drivers. In another accident prone area, the main contributing factor was reduced visibility during dust storms. Then, K-Nearest Neighbors and Random Forests machine learning algorithms were employed to predict the severity of accidents, using various input attributes such as lighting, climate, alignment slope, and road geometry. The K-Nearest Neighbor surpassed the Random Forest technique, achieving an overall accuracy of 71% in contrast to 60%. This study effectively evaluated the outcomes of clustering, uncovering the underlying causes of accidents to inform future practical interventions. Moreover, by predicting the severity of accidents along the road, a framework was developed to propose strategies for risk reduction.

Improving hierarchical models of individual differences: An extension of Goldberg's bass-ackward method.

Goldberg's (2006) bass-ackward approach to elucidating the hierarchical structure of individual differences data has been used widely to improve our understanding of the relationships among constructs of varying levels of granularity. The traditional approach has been to extract a single component or factor on the first level of the hierarchy, two on the second level, and so on, treating the correlations between adjoining levels akin to path coefficients in a hierarchical structure. This article proposes three modifications to the traditional approach with a particular focus on examining associations among all levels of the hierarchy: (a) identify and remove redundant elements that perpetuate through multiple levels of the hierarchy; (b) (optionally) identify and remove artefactual elements; and (c) plot the strongest correlations among the remaining elements to identify their hierarchical associations. Together these steps can offer a simpler and more complete picture of the underlying hierarchical structure among a set of observed variables. The rationale for each step is described, illustrated in a hypothetical example and three basic simulations, and then applied in real data. The results are compared with the traditional bass-ackward approach together with agglomerative hierarchical cluster analysis, and a basic tutorial with code is provided to apply the extended bass-ackward approach in other data. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Determinants of Government Expenditures with Health Insurance Beneficiaries in the Brazilian Health System.

Background/Objectives: The Brazilian health system provides healthcare financed by the public and private sector, being the first designed to encompass universal healthcare coverage delivered to the population without charge to patients (Sistema Único de Saúde, SUS), whilst the second refers to healthcare coverage delivered for individuals with the capacity to pay for assistance through health insurance or out-of-pocket disbursements. Health insurance companies with beneficiaries receiving publicly financed healthcare from the SUS are required to provide the reimbursement of healthcare expenditures to the government, considering that the health insurance beneficiaries obtain deductions of income taxes designed to fund the SUS. Therefore, the study investigated patterns of healthcare utilization and public expenditure due to the use of public healthcare by beneficiaries of health insurance between 2003 and 2019. Methods: Datasets including annual information on healthcare utilization by beneficiaries of health insurance from the National Agency of Supplementary Health (Agência Nacional de Saúde Suplementar, ANS) were organized into a single database to allow for the identification of patterns of interest to inform public policies of health. The empirical strategy adopted included the estimation of regression models and agglomerative hierarchical cluster analysis to identify factors associated with public sector expenditure. Results: The regression results indicated lower expenditure with female patients, particularly children and adolescents under 20 years old, receiving treatment in public sector facilities linked to the federal government. The cluster analysis showed five types of health insurance beneficiaries with a higher level of healthcare utilization, being three clusters referring to medium complexity procedures with lower public expenditures, and two clusters with higher public expenditures, one cluster that refers to high complexity procedures, and one cluster referring to health insurance schemes without hospitalization. Conclusions: The findings of the study highlight the existence of patterns of healthcare utilization by health insurance beneficiaries that may compromise the sustainability of public funding within the Brazilian health system.

Integrative multivariate analysis of agro-chemical and genotypic traits in Indian catmint (Nepeta cataria L.) half-sib populations

This investigation conducted a comprehensive multivariate analysis on thirty half-sib populations of Nepeta cataria L., derived from natural populations based on oil yield. The study addresses the lack of comprehensive data on genetic, phenotypic, and chemical diversity in Nepeta cataria, which is essential for material selection and for conducting effective breeding programs. A substantial amount of phenotypic diversity was observed for all eleven quantitative traits measured in the present study. The phenotypic diversity was assessed using Euclidean distance-based agglomerative hierarchical clustering and principal component analysis (PCA) which showed more than 50 % diversity existing among the studied populations. These methods delineated the populations into three primary clusters, where herb yield was a key discriminator. The GC and GC/MS-based chemical profiling of these populations identified eleven major phytochemicals. Though, significant variances in the concentrations of various nepetalactone isomers were noted, correlating with the phenotypic clusters, the major differentiation was observed for cis-trans Nepetalactone (73.45%–93.20 %). Three distinct chemical clusters were defined, each characterized by unique chemical signatures. Molecular diversity was evaluated using 21 Start Codon Targeted Polymorphic (SCoT) and 20 CAAT box-derived DNA Polymorphic (CBDP) markers, revealing high levels of polymorphism. Populations were again grouped into three clusters. A correlative Mantel test performed to determine the association between agro-chemical and genotypic traits revealed minimal correlation between them, indicating a complex interplay between genetic makeup and phenotypic traits across these populations. This study underscores the genetic and chemical diversity within Nepeta cataria half-sib populations, providing insights that could influence future breeding and conservation efforts. The distinct clustering based on agronomic, chemical, and genotypic data highlights the potential for targeted selection and breeding programs within this species. Future research should focus on integrating these findings into breeding strategies for enhancing commercial cultivation.

Data should be made as simple as possible but not simpler: The method chosen for dimensionality reduction and its parameters can affect the clustering of runners based on their kinematics

Dimensionality reduction is a critical step for the efficacy and efficiency of clustering analysis. Despite the multiple available methods, biomechanists have often defaulted to Principal Component Analysis (PCA). We evaluated two PCA- and one autoencoder-based dimensionality reduction methods for their data compression and reconstruction capability, assessed their effect on the output of clustering runners’ based on kinematics, and discussed their implications for the biomechanical assessment of running technique. Eighty-four participants completed a 4-minute run at 12 km/h while trunk and lower-limb kinematics were collected. Data reconstruction quality was assessed for Direct PCA (PCA directly on original variables) and Fourier PCA (modelling time series as Fourier series and then applying PCA) using popular variance explained criteria; and a feedforward autoencoder (AE). Agglomerative hierarchical clustering was then applied and the agreement between the resulting partitions was assessed. Meaningful errors in the reconstructed signals were found when applying popular variance explained criteria, suggesting reconstruction error should be assessed to make a more informed decision about how many components to retain for further analysis. Direct PCA, Fourier PCA and AE yielded different clusters, warranting caution when comparing outcomes from studies that use different dimensionality reduction techniques: each method may be sensitive to different data features. Direct PCA retaining 99 % of the original variance emerged as the best compromise of data compression, reconstruction quality and cluster separability in our dataset. We encourage biomechanists to experiment with diverse dimensionality reduction methods to optimise clustering outcomes and enhance the real-world applicability of their findings.

Implementation of ward's agglomerative hierarchical clustering model to detect pulmonary tuberculosis endemic areas in Aceh Utara regency

This study aims to detect pulmonary Tuberculosis (TB) endemic areas in Aceh Utara Regency based on altitude, population density, and the number of TB cases. The Agglomerative Hierarchical Clustering (AHC) algorithm was used to cluster 27 subdistricts, each measured by these three factors. The clustering results divided the subdistricts into three main clusters with distinct characteristics. Cluster 1 consists of subdistricts with low altitude, high population density, and relatively high numbers of TB cases, identifying this area as having the highest risk of TB endemicity. Cluster 2 includes areas with moderate population density and TB case numbers, while Cluster 3 consists of subdistricts at higher altitudes with fewer TB cases. The clustering results were evaluated using three key metrics: Silhouette Score, Davies-Bouldin Index, and Dunn Index, which indicated that the clustering model performed well, although some subdistricts were positioned near the cluster boundaries. This research provides valuable information for health authorities to prioritize interventions and allocate resources to areas most in need of TB management.

Machine learning and analytic hierarchy process integration for selecting a sustainable tractor.

Selecting the appropriate tractor for small-scale farms is a complex process due to the multitude of technical, environmental, and economic criteria that must be evaluated. This study addresses this challenge by integrating the Analytic Hierarchy Process (AHP) with machine learning (ML) to reduce the number of criteria and simplify the decision-making process. The research aims to determine the most relevant criteria aligned with sustainable development goals for selecting the right tractor, focusing on small farms in the Egyptian Delta. Four tractors, with horsepower ranging from 55 to 95, were evaluated based on inputs from forty-two governmental service providers in the study area. Initially, nine criteria were identified, encompassing key technical, environmental, and economic factors. These criteria were reduced to three-price, power, and maintenance costs by weights 0.142, 0.334, and 0.525, respectively-using Hierarchical Agglomerative Clustering with Euclidean distance. This reduction streamlined the selection process, making it more practical for farmers. Results show that the second tractor (T2), with a priority score of 0.326 and a normalized value of 33.4%, emerged as the optimal choice for small-scale farmers, outperforming the first tractor (T1) (28.7%) and third tractor (T3) (21%). Integrating AHP and ML simplifies tractor selection, ensuring sustainability, cost-efficiency, and operational effectiveness.

APPLICATION OF AGGLOMERATIVE HIERARCHICAL CLUSTERING IN GROUPING TOURISM INDUSTRY SMES IN EAST JAVA

This study addresses a significant research gap in the classification and evaluation of Small and Medium Enterprises (SMEs) within the tourism industry in East Java, specifically focusing on their marketing performance. The primary objective is to categorize these SMEs based on their marketing effectiveness, a critical factor for their competitiveness in the tourism sector. To achieve this, the research employs cluster analysis, utilizing Agglomerative Hierarchical Clustering techniques. The optimal clustering method was determined using the cophenetic correlation coefficient and cluster validation indices. Data for the analysis were collected through questionnaires, with a sample comprising 30 tourism SMEs across 11 regions in East Java, with responses from three employees per SME. The findings revealed two distinct clusters: SMEs with fairly good marketing performance and those with good marketing performance. The results suggest that while many SMEs in the tourism industry in East Java exhibit competitive marketing capabilities, there remains a subset that requires strategic improvements, particularly in spiritual marketing practices. This study's novelty lies in its approach to classifying tourism SMEs by marketing performance, providing a framework for targeted performance enhancement and industry strengthening in the region.

Efficient Multivariate Time Series Anomaly Detection Through Transfer Learning for Large-Scale Software Systems

Timely anomaly detection of multivariate time series (MTS) is of vital importance for managing large-scale software systems. However, many deep learning-based MTS anomaly detection models require long-term MTS training data to achieve optimal performance, which often conflicts with the frequent pattern changes observed in software systems. Moreover, the training overhead of vast MTS in large-scale software systems is unacceptably high. To address these issues, we design OmniTransfer , a model-agnostic framework that combines weighted hierarchical agglomerative clustering with an adaptive transfer learning strategy, making many state-of-the-art (SOTA) MTS anomaly detection models efficient and effective. Extensive experiments using real-world data from a large web content service provider and a network operator show that OmniTransfer significantly reduces the model initialization time by 46.49% and the training cost by 74.51%, while maintaining high accuracy in detecting anomalies.

Employing cluster analysis in defining groundwater wells patterns in Rafah

Clustering analysis techniques used in identifying homogeneity of groundwater wells patterns in terms of Chloride, Nitrate, and TDS. Data was collected through the laboratories of Palestinian Water Authority in the Rafah 2020. R-Programming is used for data analysis. Kolmogorov-Smirnov test is used to test data normality. Hierarchical clustering analysis applied to generate a cluster tree (r&gt;0.75) in correlation matrix. Agglomerative Hierarchical Clustering is used to classify with “average” method and found that there are two clusters. First cluster with 28 wells and Second cluster with 9 wells. Test of homogeneity between the two clusters groups using T-test for the hierarchical clustering and found that there are significant differences in means for TDS and for Chloride between first cluster and second cluster with a significant level less than 0.01. While for Nitrates, also, there are significant differences in means between the first cluster and second cluster with a significant level less than 0.05. Different stability validation measures carried out for data concludes that for the APN and ADM measures, hierarchical clustering with two clusters again gives the best score. Three internal cluster validation measures are used. Concludes that Hierarchical clustering with two clusters performs the best in (Connective, Dunn, and Silhouette). Recall that the connectivity should be minimized, while both the Dunn Index and the Silhouette Width should be maximized. Thus, Hierarchical clustering outperforms the other clustering algorithms under each validation measure.

Patterns of Adverse Childhood Experiences Among Transitional-Age Youth Experiencing Homelessness: A Cluster Analysis.

Transitional-age youth experiencing homelessness (TAY-EH) represent a high-risk population with substantial exposure to adverse childhood experiences (ACEs) and high rates of morbidity and mortality. The current study uses a person-centered analytical approach to identify patterns of ACEs among TAY-EH and inform more nuanced characterizations of this vulnerable group. Participants were aged 16-25 years accessing support at a psychosocial agency in Boston, Massachusetts. Data were gathered via structured assessment. Hierarchical agglomerative cluster analysis identified subgroups of TAY-EH with differing ACE exposures. Within the sample of 138 TAY-EH, three unique clusters of ACE exposures were identified: two high-occurrence groups (one also endorsing sexual abuse and one not), and one lower-occurrence group. Clusters differed significantly on several variables including gender identity, sexuality, and suicidality. The ways in which trauma phenotypes inform treatment access, utilization patterns, and functional outcomes should be high-priority areas for ongoing research in TAY-EH and related marginalized youth populations.