Agglomerative Hierarchical Clustering Method Research Articles

Developing ensemble clustering through similarity measures: A semi‐supervised hierarchical clustering learning

Developing ensemble clustering through similarity measures: A semi‐supervised hierarchical clustering learning

Comparative Evaluation of Six Agglomerative Hierarchical Clustering Methods With a Robust Example

The agglomerative hierarchical clustering methods are the most popular type of hierarchical clustering used to group objects in clusters based on their similarity. The methods are represented by a bottom-up approach where each object starts in its cluster and pairs of clusters are merged as it moves up the hierarchy. In this paper, we present six agglomerative hierarchical clustering methods namely: the single linkage method, complete linkage method, average linkage method, centroid method, median method, and Ward’s method. We also evaluated how these methods work on a practical basis using a matrix of distance pairs of five points. It was observed that the single linkage method through its dendrogram produced the most similarity measure between x_i and x_j, while Ward’s method produced the highest distance measure between x_i and x_j.

Strategy for improving and empowering MSMEs through grouping using the AHC method

The high number of migrants in the city of Yogyakarta has resulted in increased opportunities for Micro, Small and Medium Enterprises (MSMEs) in Culinary and Handicrafts. The large amount of data collected by the Cooperative Office, which reached thousands, caused inas to have difficulties in determining what training was needed by MSMEs and also difficulties in choosing which MSMEs would receive training held by the Cooperative Office. In addition, the Yogyakarta Cooperatives and UMKM Office had difficulties in selecting which UMKM needed to receive these trainings. Grouping can be used as a strategy in selecting MSMEs and determining training according to their individual needs. The purpose of this study was to group SMEs using the Agglomerative Hierarchical Clustering Single Linkage method and its application to provide recommendations for MSME groups to the Yogyakarta Cooperative and MSME Office. The results of the recommendations for the number of groups can be used in providing implementation, design, and evaluation of the development and empowerment of MSME data in the City of Yogyakarta. This study uses the Agglomerative Hierarchical Clustering Single Linkage method. The stages in this research are Load Data, Cleaning Data, Data Selection, Transformation Data, Clustering Process with AHC single linkage, Silhouette Coefficient, and Knowledge Representation. This research resulted in 2 group recommendations from a total of 1336 Culinary MSME data and 3 group recommendations from a total of 145 Handicraft MSME data. The results of the silhouette score test in the Culinary Sector are included in the strong structure category with a value of 0.79 and the Crafts Sector is included in the Medium Structure category with a value of 0.615. From the number of these groups, recommendations were obtained for improving a service in increasing MSMEs, especially those with a turnover of less than 10 million, marketing purposes within the Yogyakarta area, and not having financial assistance from the government. The high number of immigrants in the city of Yogyakarta has resulted in increased opportunities for Micro, Small and Medium Enterprises (MSMEs) in the Culinary and Crafts sector. The large number of MSMEs creates increasingly higher competitiveness. Apart from that, the large amount of data collected by the Department of Cooperatives and MSMEs, which reaches thousands, causes the Department to have difficulties in efforts to improve and empower these MSMEs. Grouping is one method that can be used as a strategy in mapping MSMEs, especially in efforts to improve and empower MSMEs through training conducted by the Department. The aim of this research is to group MSMEs using the Agglomerative Hierarchical Clustering (AHC) method in an effort to achieve strategies for improving and empowering MSMEs. The focus of this research is[a1] MSMEs in the craft sector and MSMEs in the culinary sector. The results of this research provide 2 group recommendations from a total of 1336 Culinary MSME data and 3 group recommendations from a total of 145 Craft MSME data. The silhouette score test results in the Culinary Sector are in the strong structure category with a value of 0.79 and in the Crafts Sector are in the Medium Structure category with a value of 0.615. From the number of groups in the two MSMEs, strategies were obtained to improve and empower MSMEs, especially those with a turnover of less than 10 million, marketing objectives within the Yogyakarta area, and not having capital assistance from the government. [a1]the result of the revision of the Abstract

A new AI-based method for clustering survey responses

ObjectivesMany research projects, particularly in social science research, depend on clustering survey responses. When analyzing survey data, traditional clustering algorithms have several drawbacks. The ability to analyze survey data more effectively has been made possible by recent developments in artificial intelligence (AI) and machine learning (ML). The aim of this article is to present a new, AI-based method of clustering survey responses using a Variational Autoencoder (VAE).Material and methodsTo determine the effectiveness of grouping, the new VAE clustering method was compared with K-means, PCA and k-means, and Agglomerative Hierarchical Clustering methods by applying the Silhouette score, the Calinski-Harabasz score, and the Davies-Bouldin score metrics.ResultsIn the case of the Silhouette Score, the developed VAE method obtained a 69% higher average effectiveness of clustering survey responses than the others. For the Calinski-Harabasz Score and the Davies-Bouldin Score, respectively, the VAE method outperformed the other methods by 164% and 111%, respectively.ConclusionsThe VAE method allowed for the most effective grouping of responses given by respondents. It has made it possible to capture complex relationships and patterns in the data. In addition, the method is suitable for analyzing different types of survey data (continuous, categorical, and mixed data) and is resistant to noise and missing data.

Inferring “high-frequent” mixed urban functions from telecom traffic

Precise distinction of mixed functions on urban land is essential for urban studies and planning, while existing methods are limited by high sampling bias, low observation frequency, and lack of semantic information in common data sources. In this paper, we introduce a new proxy for human behavior, the telecom traffic data as a remedy to the above limitations, and present an analytical framework which utilizes anonymized and aggregated telecom traffic data to infer mixed urban functions at spatiotemporal granularities as fine as buildings and hours. A time-series decomposition method is designed to map the mixture of urban functions, which is further refined by a hierarchical agglomerative clustering method taking urban textures as an additional source of information. In a case study in Shenzhen, China, we find the function of urban buildings can be decomposed into the mixture of three basic functions, namely dwelling, work, and recreation. We further find that the introduction of urban texture information helps identify particular forms of functional combination, which indicate special-function buildings such as urban villages and roadside shops. This study implies ways to improve urban management through methodological contributions in mixed urban function identification alongside the introduction of the telecom traffic, a kind of “high-frequency” urban data, and also helps inspire a rethinking of the form/function dichotomy in the era of “High-frequent” cities.

Proposal of a Visualization System for a Hierarchical Clustering Algorithm: The Visualize Proximity Matrix

Visual data mining is a new and effective strategy for dealing with the growing phenomenon of information overload. There is an urgent need for effective visual data mining because the growth of data sets from different domains and sources has made exploring, managing and analyzing vast volumes of data increasingly difficult. Identifying location-based trends and anomalies in data from a numeric output is challenging. The outputs of data mining procedures are often quite difficult to interpret. There is no human input to data exploration or direct involvement in the data mining process. Thus, it is difficult to analyze, explore, understand and view the data. In data mining, massive data sets are clustered using an iterative process that includes human input but data mining algorithms can miss some knowledge and observations. In addition, many users cannot take the appropriate action to address problems or opportunities because the huge amounts of data prevent them from staying aware of what is happening in and around their environments. Visual data mining can help in dealing with information overload and it is effective in analyzing large complex data sets. Visual data mining assists users in gaining a deep visual understanding of data. In the information age, users need to study and observe vast amounts of data to acquire important knowledge, and thus the need for visual and interactive analytical tools is particularly pressing. Visual cluster analysis has long utilized shaded similarity matrices, and this study investigated how they can be used in clustering visualization. We focused on proposing an agglomerative hierarchical clustering method. Ensemble cluster visualization is also presented for handling large data sets. This study proposes the adoption of a shaded similarity matrix to visually cluster knowledge discovered using data mining. Using the technology acceptance model as the measuring tool, we questioned respondents to evaluate the visualization prototype. The findings demonstrated that the visualization was effective and easy to use, and satisfied users.

Insights in Hierarchical Clustering of Variables for Compositional Data

R-mode hierarchical clustering is a method for forming hierarchical groups of mutually exclusive subsets of variables. This R-mode cluster method identifies interrelationships between variables which are useful for variable selection and dimension reduction. Importantly, the method is based on metric elements defined on the sample space of variables. Consequently, hierarchical clustering of compositional parts should respect the particular geometry of the simplex. In this work, the connections between concepts such as distance, cluster representative, compositional biplot, and log-ratio basis are explored within the framework of the most popular R-mode agglomerative hierarchical clustering methods. The approach is illustrated in a paleoecological study to identify groups of species sharing similar behavior.

Prediction of FKRTL Services Diagnosed with Type 2 Diabetes Mellitus Using the Hierarchical Agglomerative Clustering Time Series Method

Diabetes Mellitus (DM) is a disease characterized by hyperglycemia or increased blood sugar levels and metabolic disorders. DM is a disease with a high prevalence rate in Indonesia. DM is included in the 10 most frequent Advanced Referral Health Facility (FKRTL) visits diagnoses. This research aims to find out provincial groupings based on data from FKRTL visits of participants diagnosed with T2DM using the Hierarchical Agglomerative Clustering method (single linkage, complete linkage, average linkage) and to find out predictions of participants diagnosed with T2DM at FKRTL visits using the Autoregressive Integrated Moving Average (ARIMA) method. The method used is Hierarchical Agglomerative Clustering Time Series. This research shows that the best algorithm is average linkage with a number of clusters of two. Forecasting provides a pattern that tends to decline.

The Impact of Long-Term Conditions and Comorbidity Patterns on COVID-19 Infection and Hospitalisation: A Cohort Study

Introduction: Older adults are more vulnerable to COVID-19 infections; however, little is known about which comorbidity patterns are related to a higher risk of COVID-19 infection. This study investigated the role of long-term conditions or comorbidity patterns on COVID-19 infection and related hospitalisations. Methods: This study included 4,428 individuals from Waves 8 (2016−2017) and 9 (2018−2019) of the English Longitudinal Study of Ageing (ELSA) who also participated in the ELSA COVID-19 Substudy in 2020. Comorbidity patterns were identified using an agglomerative hierarchical clustering method. The relationships between comorbidity patterns or long-term conditions and COVID-19-related outcomes were examined using multivariable logistic regression. Results: Among a representative sample of community-dwelling older adults in England, those with cardiovascular disease (CVD) and complex comorbidities had an almost double risk of COVID-19 infection (OR = 1.87, 95% CI = 1.42−2.46) but not of COVID-19-related hospitalisation. A similar pattern was observed for the heterogeneous comorbidities cluster (OR = 1.56, 95% CI = 1.24−1.96). The individual investigations of long-term conditions with COVID-19 infection highlighted primary associations with CVD (OR = 1.46, 95% CI = 1.23−1.74), lung diseases (OR = 1.40, 95% CI = 1.17−1.69), psychiatric conditions (OR = 1.40, 95% CI = 1.16−1.68), retinopathy/eye diseases (OR = 1.39, 95% CI = 1.18−1.64), and arthritis (OR = 1.27, 95% CI = 1.09−1.48). In contrast, metabolic disorders and diagnosed diabetes were not associated with any COVID-19 outcomes. Conclusion: This study provides novel insights into the comorbidity patterns that are more vulnerable to COVID-19 infections and hospitalisations, highlighting the vulnerability of those with CVD and other complex comorbidities. These findings facilitate crucial new evidence that should be considered for appropriate screening measures and tailored interventions for older adults in the ongoing global outbreak.

Antineutrophil cytoplasmic antibody-associated vasculitis classification by cluster analysis based on clinical phenotypes: a single-center retrospective cohort study.

Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) refers to a group of small vessel inflammatory disorders. Overlapping clinical phenotypes of AAV subgroups continually provoke controversies over their diagnostic and classification criteria. Using the agglomerative hierarchical clustering method, we classified 210 Korean patients diagnosed with AAV into mutually exclusive clusters according to Birmingham Vasculitis Activity Score items, ANCA specificity, sex, and age. We analyzed the resulting clusters' outcomes to investigate the clinical significance of the classification. We proposed a distance-based algorithm of patient assignment and explored its clinically relevant modification. In total, 116 patients (55%) had microscopic polyangiitis, 53 (25%) had granulomatosis with polyangiitis, and 42 (20%) had eosinophilic granulomatosis with polyangiitis. Our model grouped the patients into five clusters, namely, "limited proteinase 3 (PR3)-ANCA vasculitis," "generalized PR3-ANCA vasculitis," "ANCA-negative vasculitis," "renal-limited vasculitis," and "myeloperoxidase-ANCA vasculitis." Patients clustered under "generalized PR3-ANCA vasculitis" had a higher relapse rate (hazard ratio [HR] = 2.12, P = 0.067). The incidence of end-stage renal disease was higher in patients belonging to the "renal-limited vasculitis" cluster (HR=1.50, P=0.03), and those in the "ANCA-negative vasculitis" cluster experienced a relatively milder clinical course of AAV (mortality = 0). Because the clusters were naturally derived from their distinguished phenotypes and have different clinical courses, our clustering method may be a more clinically relevant classification system for AAV, revealing its phenotypic diversity. We also proposed a simple and intuitive distance-based assignment algorithm, which can be easily modified according to specific clinical needs. Key Points • In this study with a single-center AAV cohort, we showed that AAV can be divided into five distinct subclasses with different disease courses based on the clinical and laboratory features of the patients. • Our study revealed ethnic differences in AAV manifestation and suggests that physicians may need to analyze their own AAV patients to assess the disease status of AAV patients. • We proposed a distance-based cluster membership assignment method that can be clinically modified to fit the specific purpose of grouping patients.

Connectomic insight into unique stroke patient recovery after rTMS treatment.

An improved understanding of the neuroplastic potential of the brain has allowed advancements in neuromodulatory treatments for acute stroke patients. However, there remains a poor understanding of individual differences in treatment-induced recovery. Individualized information on connectivity disturbances may help predict differences in treatment response and recovery phenotypes. We studied the medical data of 22 ischemic stroke patients who received MRI scans and started repetitive transcranial magnetic stimulation (rTMS) treatment on the same day. The functional and motor outcomes were assessed at admission day, 1 day after treatment, 30 days after treatment, and 90 days after treatment using four validated standardized stroke outcome scales. Each patient underwent detailed baseline connectivity analyses to identify structural and functional connectivity disturbances. An unsupervised machine learning (ML) agglomerative hierarchical clustering method was utilized to group patients according to outcomes at four-time points to identify individual phenotypes in recovery trajectory. Differences in connectivity features were examined between individual clusters. Patients were a median age of 64, 50% female, and had a median hospital length of stay of 9.5 days. A significant improvement between all time points was demonstrated post treatment in three of four validated stroke scales utilized. ML-based analyses identified distinct clusters representing unique patient trajectories for each scale. Quantitative differences were found to exist in structural and functional connectivity analyses of the motor network and subcortical structures between individual clusters which could explain these unique trajectories on the Barthel Index (BI) scale but not on other stroke scales. This study demonstrates for the first time the feasibility of using individualized connectivity analyses in differentiating unique phenotypes in rTMS treatment responses and recovery. This personalized connectomic approach may be utilized in the future to better understand patient recovery trajectories with neuromodulatory treatment.

Multimorbidity clusters and associated health care cost among patients attending psychiatric clinics in Odisha, India.

There is a dearth of data on common multimorbidity clusters and the healthcare costs for individuals with mental health disorders. This study aimed to identify clinically meaningful physical-mental multimorbidity clusters, frequently occurring clusters of conditions, and healthcare utilization patterns and expenditure among patients attending a psychiatric outpatient clinic. Data were collected in the psychiatric outpatient department among patients aged 18 years and above in February-July 2019 (n = 500); follow-up data on non-communicable disease incidence were collected after 18 months. For analysis, morbidity clusters were defined using two approaches: 1) agglomerative hierarchical clustering method to identify clusters of diseases; and 2) non-hierarchical cluster k mean analysis to identify clusters of patients. Self-reported healthcare costs in these clusters were also calculated. Two disease clusters were identified: using the 1st approach were; 1) hypertension, diabetes, and mood disorder; 2) Neurotic, stress-related, and somatoform disorders, and acid peptic disease. Three clusters of patients identified using the 2nd approach were identified: 1) those with mood disorders and cardiometabolic, musculoskeletal, and thyroid diseases; 2) those with neurotic, substance use, and organic mental disorders, cancer, and epilepsy; and 3) those with Schizophrenia. Patients in Cluster 1 were taking more than six medicines and had more hospital visits. Within 18 months, 41 participants developed either one or two chronic conditions, most commonly diabetes, hypertension, or thyroid disease. Cardiometabolic diseases are most commonly clustered with mood disorders. There is a need for blood pressure and sugar measurement in psychiatric clinics and mood disorder screening in cardiac, endocrinology, and primary care clinics.

Agglomerative Hierarchical Clustering (AHC) Method for Data Mining Sales Product Clustering

Supermarkets are Indonesian terms that refer to large stores or supermarkets that offer a variety of daily needs such as food, drinks, cleaning products, household appliances, clothing, and so on. In contrast to stalls or small shops, supermarkets have a larger size and provide a variety of products. Because of this, many people prefer to shop for their daily needs at the supermarket rather than at the nearest shop because the existence of the supermarket makes it easier for consumers to buy various products in one place without having to move to another store. However, sales in supermarkets also pose a problem, namely how to sort or group products that are not selling well so they can be replaced with products that are selling better or reduce the number of suppliers. This is where data mining or data analysis techniques that use business intelligence are needed. The research was conducted to classify the best-selling products in supermarkets using the Agglomerative Hierarchical Clustering (AHC) method, in which alternatives with the same matrix or distance are grouped into certain clusters. In applying the AHC method, the number of clusters formed is 3. There are three different clusters, namely cluster 0, cluster 1, and cluster 2, each with a different alternative group. Each cluster has a different number of products and a different percentage. Cluster 0 is the cluster with the highest number of products and the largest percentage, namely 45% with a total of 9 products, followed by cluster 2, and cluster 1 has the smallest number of products and percentage, namely 0.30% with a total of 6 products and 0 .25% with a total of 5 products. In addition, sales data for several products each month are grouped based on certain price ranges

IMPLEMENTASI METODE AGGLOMERATIVE HIERARCHICAL CLUSTERING UNTUK SISTEM REKOMENDASI FILM

People can now watch movies on their cellphones or other devices using applications, in addition to watching them on television or in theaters. The user's entered keywords are used as the basis for a system that suggests movies from among the many that have appeared over time. Later, similarity between these keywords and text data, such as movie titles and descriptions, will be assessed. This recommendation system will include preprocessing, and the TF-IDF method will be used to determine the weight value. After the weight values have been determined, the grouping calculations will be performed using agglomerative hierarchical clustering. Previously, the Manhattan Distance method will be used to calculate the distance. After that, the distance that is closest can be determined. The data will be clustered according to the shortest distance once the distance calculation is complete. Following that, the system will display the grouping as a dendrogram. The data used was updated as of the date of scraping, which is November 25, 2022, and contains a total of 2467 data. The Agglomerative Hierarchical Clustering method yielded the best silhouette coefficient value, 0.5025559374455285, forming 20 clusters.

Estimating the electric vehicle charging demand of multi-unit dwelling residents in the United States

Early battery electric vehicle (EV) adopters can access home chargers for reliable charging. As the EV market grows, residents of multi-unit dwellings (MUDs) may face barriers in owning EVs and charging them without garage or parking availability. To investigate the mechanisms that can bridge existing disparities in home charging and station deployment, we characterized the travel behavior of MUD residents and estimated their EV residential charging demand. This study classifies the travel patterns of MUD residents by fusing trip diary data from the National Household Travel Survey and housing features from the American Housing Survey. A hierarchical agglomerative clustering method was used to cluster apartment complex residents’ travel profiles, considering attributes such as dwell time, daily vehicle miles traveled (VMT), income, and their residences’ US census division. We propose a charging decision model to determine the charging station placement demand in MUDs and the charging energy volume expected to be consumed, assuming that MUD drivers universally operate EVs in urban communities. Numerical experiments were conducted to gain insight into the charging demand of MUD residents in the US. We found that charging availability is indispensable for households that set out to meet 80% state of charge by the end of the day. When maintaining a 20% comfortable state of charge the entire day, the higher the VMT are, the greater the share of charging demand and the greater the energy use in MUD chargers. The upper-income group requires a greater share of MUD charging and greater daily kWh charged because of more VMT.

Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning

Switches and crossings (S&Cs) are also known as turnouts or railway points. They are important assets in railway infrastructures and a defect in such a critical asset might lead to a long delay for the railway network and decrease the quality of service. A squat is a common rail head defect for S&Cs and needs to be detected and monitored as early as possible to avoid costly emergent maintenance activities and enhance both the reliability and availability of the railway system. Squats on the switchblade could even potentially cause the blade to break and cause a derailment. This study presented a method to collect and process vibration data at the point machine with accelerometers on three axes to extract useful features. The two most important features, the number of peaks and the total power, were found. Three different unsupervised machine learning algorithms were applied to cluster the data. The results showed that the presented method could provide promising features. The k-means and the agglomerative hierarchical clustering methods are suitable for this data set. The density-based spatial clustering of applications with noise (DBSCAN) encounters some challenges.

Distrust Behavior in Social Network Large-Scale Group Decision Making and Its Application in Water Pollution Management

Distrust behavior is a human behavior that has a significant impact on water pollution management, but it is neglected in existing approaches. To solve this problem, we design a large-scale group decision making in social network (LSGDM-SN) approach based on distrust behavior and apply it to water pollution management. The purpose of this paper is to develop an LSGDM-SN method to assist managers choose the optimal water pollution management plan. In the presented method, fuzzy preference relations (FPRs) are used to express experts’ assessment of alternatives. To utilize the proposed LSGDM-SN approach to solve the water pollution problem, a novel agglomerative hierarchical clustering (AHC) method is proposed by combing preference similarity and social relationships. Afterward, consensus feedback based on distrust behavior and social network analysis (SNA) is developed to encourage the subset to modify its FPR. A mechanism for the identification and management of distrust behavior is introduced. Based on the situations of distrust behaviors, two pieces of feedback advice are provided to the subset to adjust its FPR. Subsequently, a score function of the FPR is proposed to obtain the best solution for water pollution management. Finally, some comparative analyses and discussions demonstrate the effectiveness and feasibility of the proposed method.

Intelligent Reflecting Surface-Aided Integrated Terrestrial-Satellite Networks

Intelligent reflecting surface (IRS) is a novel technology to manipulate wireless propagation channels via smart and controllable signal reflection. In this paper, we investigate an IRS-aided integrated terrestrial-satellite network (ITSN) system, where the IRS is deployed to assist the co-existing transmissions of the terrestrial small base stations (SBSs) and the satellite. Because of the spectrum sharing in the ITSN, the interference between the two systems should be carefully mitigated. Our objective is to maximize the weighted sum rate (WSR) of all users by jointly optimizing the frame-based coordinated transmit beamforming vectors at the SBSs, the phase shift matrix at the IRS, and the frame user scheduling, subject to SBSs’ individual power constraints and unit modulus constraints of phase shifters. To this end, we first adopt the agglomerative hierarchical clustering (AHC) method to schedule the satellite users to different frames. Then the block coordinate descent (BCD) algorithm is proposed, which alternately optimizes the transmit beamforming vectors and the reflective phase shift matrix. In particular, the optimal transmit beamforming vectors are obtained via the fractional programming (FP) technique. Meanwhile, two efficient algorithms, i.e., the Riemannian manifold (RM) and the successive convex approximation (SCA), are proposed for the phase shift optimization. Finally, simulation results are provided to demonstrate the performance gain of our schemes over other benchmark schemes.

The impact of isolation kernel on agglomerative hierarchical clustering algorithms

Agglomerative hierarchical clustering (AHC) is one of the popular clustering approaches. AHC generates a dendrogram that provides richer information and insights from a dataset than partitioning clustering. However, a major problem with existing distance-based AHC methods is: it fails to effectively identify adjacent clusters with varied densities, regardless of the cluster extraction methods applied to the resultant dendrogram. This paper aims to reveal the root cause of this issue and provides a solution by using a data-dependent kernel. We analyse the condition under which existing AHC methods fail to effectively extract clusters, and give the reason why the data-dependent kernel is an effective remedy. This leads to a new approach to kernerlise existing hierarchical clustering algorithms including the traditional AHC algorithms, HDBSCAN, GDL, PHA and HC-OT. Our extensive empirical evaluation shows that the recently introduced Isolation Kernel produces a higher quality or purer dendrogram than distance, Gaussian Kernel and adaptive Gaussian Kernel in all the above mentioned AHC algorithms.

Phenotypic Variability for Root Traits in Andean Common Beans Grown with and without Aluminum Stress Conditions

Genetic variation in wild relatives, GenBank accessions, landraces, and cultivars can unlock key alleles for the traits of interest for breeding programs. Breeding programs often utilize different strategies to quantify the source of heritable variation for target traits. One neglected area of study is the root traits of diverse genotypes, and this is especially the case for aluminum toxicity effects on legumes such as the common bean, which is the most used pulse for direct human consumption. This study evaluated 267 genotypes of common bean that were part of the global Andean Diversity Panel (ADP), consisting mainly of genotypes assembled from public and private breeding programs in Africa and North America, as well as elite lines and land races from the USDA. The ADP was evaluated for root traits at the seedling stage in the Tennessee State University (TSU) greenhouse using a hydroponic system with a standard nutrient solution with and without aluminum (Al). The recorded data on the roots per trial were fit to a linear mixed model for the analysis of variance in order to test for the genotype differences. Adjusted means considered replication and blocks within replication as random effects and genotypes as fixed effects. These were then used for Pearson correlation tests and for principal component analysis (PCA), where the first two vectors accounted for 94.5% and 93% of the explained variation under the control and Al-treatment conditions, respectively. Genotypes were clustered based on the morphology of roots in response to Al-toxicity treatment using the Euclidean distance and Ward’s hierarchical agglomerative clustering method, identifying four distinctive groups significant at p < 0.01. The intra-cluster distance was lower than the inter-cluster distances, which indicated a heterogeneous and homogeneous nature between and within clusters, respectively. The results suggest that crossing between accessions from two of the clusters would result in the maximum genetic segregation. One cluster was found to have a higher Al-toxicity tolerance than the others.