Agglomerative Hierarchical Clustering Technique Research Articles

Impact of Dataset Scaling on Hierarchical Clustering: A Comparative Analysis of Distance-Based and Ratio-Based Methods

In this study, the distance-based agglomerative hierarchical clustering techniques were compared to a ratio-based approach. Two real datasets, which were also used in a prior study by Roux (2018), were considered. Firstly, it was observed that the type of scaling applied to the datasets was found to affect the results of hierarchical clustering. Thus, various scaling methods were employed prior to implementing hierarchical clustering. Furthermore, two rank-based goodness-of-fit measures were used to evaluate the hierarchical clustering methods. In contrast to Roux (2018) findings, it was observed that the distance-based methods, such as Median linkage, Average linkage, and centroid linkage, performed better than the ratio-based method. The ratio-based methods also showed issues with branch crossing in the hierarchical clustering dendrogram. Consequently, this study illustrates that, with appropriate dataset scaling, the distance-based methods outperform ratio-based methods in terms of goodness-of-fit measures.

K-MEANS AND AGGLOMERATIVE HIERARCHICAL CLUSTERING ANALYSIS OF ESG SCORES, YEARLY VARIATIONS, AND STOCK RETURNS: INSIGHTS FROM THE ENERGY SECTOR IN EUROPE AND THE UNITED STATES

This study employs k-means clustering and agglomerative hierarchical clustering techniques to visually examine the potential relationship between Environmental Social and Governance (ESG) scores, their year-over-year variations, and annual stock returns for a sample of 34 energy sector companies operating in Europe and the United States. While the agglomerative hierarchical clustering dendrogram suggests two clusters, the elbow method of the k-means algorithm suggests 2-4 clusters. The results indicate that neither ESG scores nor their year-on-year variations had an impact on the annual returns of the stocks. The conclusion is further confirmed by the Pearson correlation coefficient. However, the ESG scores of European energy companies show a tighter dispersion and smaller year-over-year change, making them more predictable ESG score-wise and thus, potentially, more attractive to ESG-driven investors.

Extracting Patterns of Proximity in Regional Development Inequality Using Hierarchical Agglomerative Clustering

Equitable regional development is still difficult to implement. Many interests in determining regional development priorities make it difficult to realize the main goals of national development. Political interests and the underutilization of regional statistical data are often other obstacles in carrying out regional development. This study aims to classify regional development data to be used to analyse existing development inequalities. Gross regional domestic income (GRDP) data is the main data in this study. The researcher used the hierarchical agglomerative clustering (HAC) technique. The results of the cluster can be used as a consideration for local governments to determine future regional development priorities. In addition, visualized cluster results in the form of a dendrogram can show the proximity of development inequality between regions.

Hierarchical clustering with spatial adjacency constraints in heavy-tailed areal data

Some natural phenomena with areal/lattice data structures include extreme values or outliers. In such situations, the assumption of Gaussianity for the random field may not be reasonable, and no Gaussian transformation can be found because they exhibit heavy tails. A non-Gaussian stable random field, which is heavy-tailed, could be a more appropriate choice in these cases. This article introduces a sub-Gaussian α-stable (SGαS) random field for spatial analysis of multivariate areal data using a multivariate conditional autoregressive model. We, specifically, focus on the spatial clustering problem of such areal data. To address it, we develop methods that work based on adjacency constraints. To group the data, we offer an adjacency-constrained hierarchical agglomerative clustering (HAC) technique that considers both spatial and non-spatial attributes existing in the multivariate areal data. The proposed clustering algorithm is developed based on spatial adjacency constraint criteria incorporated into the HAC technique. We employed the developed algorithm to group Luxembourg communes based on simulated areal data from the SGαS distributions and French cities along the Gironde estuary based on the estuary areal dataset. We compare the results of the proposed method with the mentioned adjacency criteria, various dissimilarity, and linkages measures in clustering these two datasets.

Clustering Strategies for Defining Archetypes to Support Integrated Simulations of Environmental Impacts

Life cycle assessment (LCA)’s inherent data-intensiveness hampers application to neighbourhood environmental assessments, particularly for built stock modelling. Data collection can be reduced to a manageable amount by grouping a large number of buildings into a limited set of aggregates with similar characteristics and defining exemplars (archetypes) that represent each group. LCAs would be performed for the archetypes only and their results extended to the represented buildings. As archetype definition is seldom detailed in the literature, this paper tests, and details different procedures that could enable neighbourhood LCAs. K-medoids and CLARA partition algorithms, as well as agglomerative hierarchical clustering techniques, were applied to group over 300 buildings into a limited number of clusters. A building representative of each cluster was identified to proceed to bottom-up LCA. K-medoids clustering stands out for the quality of clusters and their representatives. Restraining the maximum number of clusters to keep subsequent LCA work manageable imposes some quality loss yet allows for achieving satisfactory division results. Regardless of the clustering technique used, data was the best divided the larger the number of clusters used, for the various factors in the database depicting the studied area resulted in several possible data combinations. Although detailed representation is desirable in LCA modelling, limiting the number of variables facilitates data pre-treatment and an optimal balance should be pursued in future studies.

A Framework for an Efficient Recommendation System Using Time and Fairness Constraint Based Web Usage Mining Technique

Users prefer to use various websites like Facebook, Gmail, and YouTube. We can make the system predict what pages we expect in the future and give the users what they have requested. Based on the data gathered and analyzed, we can predict the user's future navigation patterns in response to the user's requests. In order to track down users’ navigational sessions, the web access logs created at a specific website are processed. Grouping the user session data is then done into clusters, where inter-cluster similarities are minimized, although the intra-cluster similarities are maximised. Recent clustering and fairness analysis research has focused on centric-based methods such as k-median and k-means clustering. We propose improved constrained based clustering (ICBC) based on fair algorithms for managing Hierarchical Agglomerative Clustering (HAC) that apply fairness constraints regardless of distance linking parameters, simplifying clustering fairness trials for HAC and intended for various protected groups compared to vanilla HAC techniques. Also, this ICBC is used to select an algorithm whose inherent bias matches a specific problem, and then to adjust the optimization criterion of any distinct algorithm to take the constraints on interpretation to improve the efficiency of clustering. We show that our proposed algorithm finds fairer clustering by evaluation on the NASA dataset by balancing the constraints of the problem.

Temporal Clustering of the Causes of Death for Mortality Modelling

Actuaries utilize demographic features such as mortality and longevity rates for pricing, valuation, and reserving life insurance and pension contracts. Capturing accurate mortality estimates requires factual mortality assumptions in mortality models. However, the dynamic and uncertain nature of mortality improvements and deteriorations necessitates better approaches in tracking mortality changes, for instance, using the causes of deaths features. This paper aims to determine temporal homogeneous clusters using unsupervised learning, a clustering approach to group causes of death based on (dis)similarity measures to set representative clusters in detection and monitoring death trends. The causes of death dataset were derived from the World Health Organization, Global Health Estimates for males and females, from 2000 to 2019, for Kenya. A hierarchical agglomerative clustering technique was implemented with modified Dynamic Time Warping distance criteria. Between 6 and 14 clusters were optimally achieved for both males and females. Using visualisations, principal clusters were detected. Over time, the causes of death trends of these clusters have demonstrated a correlated association with mortality and longevity rates, rationalizing why insurance and pension offices may include this approach as a preliminary step to undertake mortality and longevity modelling.

Minimum spanning tree hierarchical clustering algorithm: A new Pythagorean fuzzy similarity measure for the analysis of functional brain networks

Clustering structures are one of the most important aspects of complex networks. Minimum spanning tree (MST), the tree that connects all vertices with minimum total weight, can be considered as a fundamental unit of original weighted graphs. There are different types of algorithms that identify clusters in a network, but the existing theories and algorithms for searching trees have not been investigated for uncertain scenarios. This paper tackles the situations where network parameters may be uncertain. Rather, we permit the parameters to take the form of Pythagorean fuzzy numbers (PFNs). Moreover, to represent qualitative aspects of uncertainty, the use of linguistic variables (LVs) has effective means for experts in expressing their views. The current study proposes a graph theory-based agglomerative hierarchical clustering technique for Pythagorean fuzzy sets. We first define generalized Pythagorean fuzzy numbers (GPFNs) and LR-type PFNs. Then we compute the PF distance between two GPFNs and also LR-type PFNs, and formulate the expressions for PF similarity measure. This paper mainly examines the use of PF distance and similarity measures in a minimum spanning tree agglomerative hierarchical clustering method by considering PFLVs. Since the structural and functional systems of brain are characterized by complex networks, we apply the proposed algorithm on a functional brain network to prove its practicality and efficiency. We discuss the hierarchical clustering consequences of the proposed algorithm in the shape of a dendrogram. Finally, we compare the clustering results obtained by different similarity measures.

A novel approach for optimal cabling and determination of suitable topology of MTDC connected offshore wind farm cluster

The advancement of technologies has shifted focus from onshore to offshore wind farms (OWFs) to extract the maximum benefit out of wind resources. Capital investment for OWFs is high; hence, proper planning is inevitable to utilize resources efficiently. This paper proposes a novel two-level planning approach, where in the first level, optimal cable connection of OWFs has been determined. In the second level, reliability and economic evaluation of different configurations of high voltage DC (HVDC) connected OWFs have been studied. Three OWFs of different shapes and ratings, however, with the same wind profile and connected to onshore voltage source converter- HVDC (VSC- HVDC) platform through HVDC link, have been considered in this study. In the first level, the placement of wind turbines (WTs) in the individual OWF is optimized using particle swarm optimization. Following that, an optimal combination of WT clusters of OWFs is obtained using the agglomerative hierarchical clustering technique. In this context, two objectives, such as economy and reliability have been considered. The traveling salesman problem is formulated to obtain minimized cable lengths inside each WT cluster of the OWF. The cost of submarine cable is used to evaluate the economic aspect, whereas expected energy not supplied (EENS) is taken as the reliability index. A sequential Monte Carlo method has been developed to calculate EENS considering random outage of WTs and cables. In the next level, an economic and reliability assessment of the entire multi-terminal DC (MTDC) OWF cluster is performed. Economic assessment is performed considering the total cost of different MTDC components, whereas, forced outage of the components is considered to assess the reliability index. A comparative study of different connection topologies of MTDC OWF configurations shows that mesh connection has the highest reliability and availability (94.97%). On the other hand, multi-terminal topology has the least investment cost which is 12.40% and 7.51% more economical compared to mesh and radial topology respectively.

Fishing opportunities for the deepwater cardinalfish ( Epigonus crassicaudus ) in the continental shelf break along central Chile

Spatial patterns in fish distribution determine most fishing opportunities and tactics learned by fishers. To determine fishing opportunities and recurrent fishing grounds of deepwater fish such as the cardinalfish (Epigonus crassicaudus) in the south-central zone of Chile, we analyzed records of fishing logs covering the period 1999–2007. Fishing trawls covered from Topocalma (34º06'S) to northwest Corral (39º52'S), and we compared the position of individual fishing hauls (Euclidean distance) applying a hierarchical agglomerative clustering technique. The clusters obtained represented spatially distributed fishing opportunities (n = 28), which were visited occasionally (<10 fishing hauls) or regularly (>50 fishing hauls) to fishing for cardinalfish. The recurrent fishing opportunities (n = 7) were distributed discretely, from Constitución (35°19'S) to northwest Mocha Island (38°23'S) along the continental shelf slope and between 313 and 333 m depth. The species composition in the bycatch and the catch per unit effort trends allow us to identify secondary species and locally depleted fishing opportunities, the last occurring close to main fishing ports.

Swarm Intelligence-based Hierarchical Clustering for Identification of ncRNA using Covariance Search Model

Covariance Model (CM) has been quite effective in finding potential members of existing families of non-coding Ribonucleic Acid (ncRNA) identification and has provided ex-cellent accuracy in genome sequence database. However, it has significant drawbacks with family-specific search. An existing Hierarchical Agglomerative Clustering (HAC) technique merged overlapping sequences which is known as combined CM (CCM). However, the structural information will be discarded, and the sequence features of each family will be significantly diluted as the number of original structures increases. Additionally, it can only find members of the existing families and is not useful in finding potential members of novel ncRNA families. Furthermore, it is also important to construct generic sequence models which can be used to recognize new potential members of novel ncRNA families and define unknown ncRNA sequence as the potential members for known families. To achieve these objectives, this study proposes to implement Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to ensure the CCMs have the best quality for every level of dendrogram hierarchy. This study will also apply distance matrix as the criteria to measure the compatibility between two CMs. The proposed techniques will be using five gene families with fifty sequences from each family from Rfam database which will be divided into training and testing dataset to test CMs combination method. The proposed techniques will be compared to the existing HAC in terms of identification accuracy, sum of bit-scores, and processing time, where each of these performance measurements will be statistically validated.

Hybrid Cluster based Collaborative Filtering using Firefly and Agglomerative Hierarchical Clustering

Recommendation Systems finds the user preferences based on the purchase history of an individual using data mining and machine learning techniques. To reduce the time taken for computation Recommendation systems generally use a pre-processing technique which in turn helps to increase high low performance and over comes over-fitting of data. In this paper, we propose a hybrid collaborative filtering algorithm using firefly and agglomerative hierarchical clustering technique with priority queue and Principle Component Analysis (PCA). We applied our hybrid algorithm on movielens dataset and used Pearson Correlation to obtain Top N recommendations. Experimental results show that the our algorithm delivers accurate and reliable recommendations showing high performance when compared with existing algorithms.

A clustering solution for analyzing residential water consumption patterns

Water utility companies in urban areas face two major challenges: ensuring there is enough water for everyone during prolonged drought and maintaining adequate water pressure during the hours of peak demand. These issues can be overcome by applying data analytics and machine learning to the data gathered from digital water meters. For water conservation and demand management strategies to be effective, utility companies need to gain a better understanding of consumer behaviours, habits and routines. To accomplish this goal, we adapted a clustering approach to reveal residential water consumption patterns within metered data. In the experiment, we used two data sets (engineered features data set as well as the times of use and weighted probabilities of use data set) based on the data collected over 10 months from 306 households in Melbourne, Australia. For the engineered features data set, first, we identified the number of optimal clusters. We then performed extensive experiments to find the best clustering approach in terms of performance evaluation and clustering quality. We chose the hierarchical agglomerative clustering technique based on the nature of the data and the objective of the study. We observed that for the engineered features data set, k-means is the best performing clustering technique after considering performance metrics. For the other data set, we found that the number of clusters varies based on the type of water-consumption event, type of day (i.e., weekday or weekend), profiling interval and probability of use. In addition, we observed that insight into tap-water usage could be used to determine the population’s adaptation of hygiene practices in an unprecedented time, such as the COVID-19 pandemic. Finally, we recommend that future clustering studies also employ aligned socio-demographic data and other key features.

Assessment of precipitation trends and its implications in the semi-arid region of Southern India

Rainfall is a complex meteorological phenomenon with high spatio-temporal variability. These variations in hydrologic time series can be better analyzed using homogeneity and trend tests. In this paper, three homogeneity tests were applied to the annual rainfall series of 1980–2013 from 30 raingauge stations over the study area. The rainfall stations with homogeneous time series were subjected to cluster analysis (Agglomerative Hierarchical Clustering technique) and thereafter traditional trend tests (Mann-Kendall test and Spearman Rank-order Correlation test) and one emerging trend test (called ‘Innovative Trend test’) were applied to individual clusters. Based on the homogeneity test results, 27 rainfall stations with homogeneous time series were classified under six clusters. The results of the trend analysis revealed that a distinct pattern of increasing (decreasing) rainfall trend exists in the northern (southern) part of the study area. Although the traditional tests detected insignificant trends, the Innovative Trend (IT) test indicated the trends to be significant in all the clusters. However, the magnitude of trends detected by the traditional trend tests and non-traditional trend tests is similar for all the clusters. It is concluded that the emerging IT test is highly sensitive to the changes in the rainfall time series. The findings of this study suggest that the orography, population density, industrialization and agricultural intensity lead to a precipitation gradient in the study area.

Age and gender differences in physical heart failure symptom clusters

BackgroundGender differences exist in structure and function of the heart resulting in HF symptom variation. Previous HF symptom cluster research described symptom clusters that were linked to functional status, mortality, quality of life and rehospitalization. Age and gender differences between cluster groups were described in one study. ObjectivesIdentify physical HF symptom clusters and explore age and gender differences between clusters. MethodsSecondary analysis study of adults with HF. Cluster analysis was conducted using hierarchical agglomerative clustering techniques. A pictorial dendrogram output displays clusters. ResultsThree symptom clusters were identified in this sample of 133 older HF patients that differed by gender (p = 0.04), age (p = 0.00) and beta blocker use (p = 0.01). Symptom clusters were consistent with worsening HF, acute HF and chronic HF. ConclusionSymptom clusters differ by age and gender. Education should be directed at increasing patient awareness of their individual symptom clusters.

Delineating Precipitation Regions of the Contiguous United States from Cluster Analyzed Gridded Data

Spatially homogenous precipitation regions were delineated for the contiguous United States using a gridded data set of daily precipitation. Seasonal means (1981–2010) of four variables, together characterizing seasonal precipitation, were computed and subjected to a principal component analysis (PCA). PCA reduced the original 30,665 grid cells by sixteen precipitation variables (four variables, four seasons) in the data set. The standardized scores of the three retained principal components, which together retain 78.4 percent of the original data set’s variance, were then subjected to three agglomerative hierarchical clustering techniques. Using an objective method, several cluster solutions were examined, and the average linkage thirteen-cluster solution was deemed optimal. The average linkage solution was then subjected to a k-means partitioning technique under the premise that objects are not considered for reassignment during agglomerative hierarchical cluster procedures. The result is fifteen precipitation regions across the contiguous United States. Results indicate that the regions successfully minimize intraregion variability and maximize interregion variability when compared to the nine climate regions defined by the United States National Centers for Environmental Information. It is therefore suggested that the regions defined by this work will better serve research aimed at an improved understanding of long-term hydroclimate change and variability at regional to synoptic scales across the United States.

DELINEATION OF SITE-SPECIFIC MANAGEMENT ZONES USING MULTIVARIATE ANALYSIS AND GEOGRAPHIC INFORMATION SYSTEM TECHNIQUE

The basic objectives of this study were: 1) The ability to characterize the spatial variability across a soil for selected soil properties using GIS technique and 2) identification of site-specific management zones using selected soil properties using PCA and cluster analysis. 120 geo-referenced representative soil samples obtained from Sahl AlHussainiyah, El-Sharkia Governorate, Egypt, (from 0 to 0.60 m depth). These samples were prepared and analysed for soil characteristics, such as soil pH, electrical conductivity (ECe), calcium carbonate (CaCO3 ), soil organic matter (OM), available N, P, K, soil cations exchange capacity (CEC), and bulk density (BD). Using semi-variogram analysis and ordinary kriging, spatial distribution pattern varies from moderate to strong spatial dependence for most soil characteristics. Using PCA and cluster analysis, site-specific management zones were identified in the study area. For further analysis, four PCs with eigenvalues &gt; 1 were used, with PCs explaining 73.19 percent of the variance. Four MZs were defined based on cluster analysis using an agglomerative hierarchical clustering technique. The differences between these MZs were statistically significant (p &lt; 0.05). 3.01 (466.56 ha), 36.47 (5658.9 ha), 31.02 (4813.54 ha), and 29.5 percent (4577.8 ha) of the total area is MZ1, MZ2, MZ3, and MZ4 (15516.8 ha).

A Hybrid Model to Classify Patients with Chronic Obstructive Respiratory Diseases.

Over the last decades, an increase in the ageing population and age-related diseases has been observed, with the increase in healthcare costs. As so, new solutions to provide more efficient and affordable support to this group of patients are needed. Such solutions should never discard the user and instead should focus on promoting more healthy lifestyles and provide tools for patients’ active participation in the treatment and management of their diseases. In this concern, the Personal Health Empowerment (PHE) project presented in this paper aims to empower patients to monitor and improve their health, using personal data and technology assisted coaching. The work described in this paper focuses on defining an approach for user modelling on patients with chronic obstructive respiratory diseases using a hybrid modelling approach to identify different groups of users. A classification model with 90.4% prediction accuracy was generated combining agglomerative hierarchical clustering and decision tree classification techniques. Furthermore, this model identified 5 clusters which describe characteristics of 5 different types of users according to 7 generated rules. With the modelling approach defined in this study, a personalized coaching solution will be built considering patients with different necessities and capabilities and adapting the support provided, enabling the recognition of early signs of exacerbations and objective self-monitoring and treatment of the disease. The novel factor of this approach resides in the possibility to integrate personalized coaching technologies adapted to each kind of user within a smartphone-based application resulting in a reliable and affordable alternative for patients to manage their disease.

UAV-Thermal imaging and agglomerative hierarchical clustering techniques to evaluate and rank physiological performance of wheat genotypes on sodic soil

Sodicity is a major soil constraint in many arid and semi-arid regions worldwide, including Australia, which adversely affects the ability of crops to take up water and nutrients from the soil, reducing yield. Reliable methods and tools are required for appropriate selection of traits, may provide a better understanding of crop responses to multiple stresses, especially in sodic soil. A novel strategy was developed using unmanned aerial vehicle (UAV)-thermal imaging and agglomerative hierarchical clustering-based techniques to evaluate and rank the physiological performance of 18 contrasting wheat genotypes grown on a moderately sodic and a highly sodic soil in north-eastern Australia. We obtained UAV-thermal imaging data at different times of the day (9:30, 12:00, and 15:00 hrs) close to flowering stage. Crop biophysical parameters (Leaf potassium concentration, normalized difference vegetation index, crop water uptake, stomatal conductance, plant moisture content, and aboveground biomass) were measured at close to flowering by destructive plant sampling and ground-based proximal sensing and yield was machine harvested at maturity. Canopy temperatures derived from thermal imagery between 28.9 and 35.4 °C were observed at the moderately sodic site, and between 36.2 and 41.0 °C at the highly sodic site from 9:30 to 15:00 hrs. Canopy temperature was consistently higher than corresponding ambient air temperatures indicating plant water stress at both sites. While the air temperature was not significantly different (p > 0.05) between the two sites, canopy temperature was significantly higher (p < 0.01) on highly sodic soil compared to moderately sodic soil, indicating greater water stress at the highly sodic site. This difference was most likely due to the adverse impacts of sodic soil constraints and not primarily due to environmental variations. Hence, our study revealed that sodic soil constraints can intensify plant water stress. Statistical analysis between canopy temperature (9:30, 12:00, and 15:00 hrs) and crop biophysical parameters showed close negative correlations at both moderately sodic (R2 = 0.54 to 0.83) and highly sodic (R2 = 0.30 to 0.89) sites. A closer correlation was observed at 15:00 hrs for both sites. Thus, high-resolution UAV-thermal imaging has potential to detect water-stressed plants on sodic soil. Agglomerative hierarchical clustering was used as an unsupervised machine learning tool for ranking of physiological performance of wheat genotypes. Results suggest that UAV-thermal imaging and AHC techniques can discriminate cultivars tolerant to sodicity. The study improves our understanding of crop physiological behaviour and can assist farmers in selection of water stress tolerant genotypes to sustain food security in sodic soil under water-limited environments.

Levels of Illness Uncertainty Vary by Heart Failure Symptom Cluster Group

Background Heart failure patients experience multiple concurrent symptoms called symptom clusters. Understanding the role and characteristics of symptom clusters in heart failure (HF) patients is limited. Illness uncertainty may lead to difficulty interpreting symptoms and timely treatment. Before person-centered interventions can be developed, more information about variations in HF symptom clusters and factors associated with them are needed. Purpose The purpose of this study was to describe HF symptom clusters and determine if illness uncertainty levels differed by symptom cluster. Methods A total of 129 HF patients (mean age 76.4 (12.4) years, education 10.6 (3.2) years and time with HF 3.6 (5.9) years, 54% male, 93% Caucasian, 46% widowed/divorced, NYHA class III) completed the Heart Failure Somatic Perception Scale (HFSPS, 0-90, higher score indicates more symptoms) and Mishel's Uncertainty in Illness Scale (MUIS, 23-115, higher score indicates more uncertainty) during hospitalization. Symptom clusters were determined by hierarchical agglomerative clustering techniques in SPSS 25. After controlling for differences in age, gender and length of time with HF, ANCOVA (post hoc LSD) analyses explored uncertainty levels by symptom cluster group. Results Three clusters were found: 1. Shortness of breath cluster (SOB), n = 43 (fatigue, hard to breathe, SOB when dressing, orthopnea, DOE, PND, rest during day, wake urinate at night), 2. Edema cluster, n = 47 (swollen feet, tight shoes, tight clothes, weight gain), 3. Cardiac symptom cluster, n = 39 (palpitations, chest pain, upset stomach, decreased appetite, cough). After adjusting for age, gender and length of time with HF, there was a significant difference in uncertainty levels by symptom cluster group (p = .000). Post hoc analysis demonstrated that cardiac cluster members reported less illness uncertainty than edema cluster members (69.7 vs 75, respectively, p = .033). Conclusions Results suggest that significant differences exist in illness uncertainty levels based on the symptom experience of patients with HF. Care and management of HF symptoms should include a complete assessment of unique symptom cluster profiles.