Agglomerative Hierarchical Clustering Algorithm Research Articles

Comparative Analysis Clustering Algorithm for Government’s Budget Performance Data

The government's budget performance is a benchmark for the government's success in optimizing people's money to achieve national goals. Even though performance measurement has reached the Work Unit level, the data formed still do not have a specific grouping, in the sense of unstructured data. The purpose of this research is to find the best clustering algorithm for classifying budget performance data. The data used is budget performance data for 19,460 Indonesian Government Work Units. The data is sourced from the SMART application and the OM SPAN application. This research uses a comparative study approach for the K-Means algorithm, DBSCAN, and agglomerative hierarchical clustering (AHC). Evaluation of the clustering results formed using the Davies-Bouldin Index (DBI) method. The AHC algorithm with k = 6 achieved the lowest DBI value of 0.3583472. The DBI value for the DBSCAN algorithm with MinPts = 10 is 0.5398259. However, the AHC algorithm is not good in terms of ease of implementation. Therefore, the K-means algorithm with parameters k = 10 is the best alternative. The K-Means algorithm gets a DBI value of 1.052678. The K-Means algorithm produces 10 clusters. Based on knowledge extraction, it is determined that cluster 2 and cluster 5 are ideal clusters in terms of budget performance. While the clusters that require attention are cluster 1, cluster 3, cluster 4, and cluster 8.

Comparative Analysis Clustering Algorithm for Government’s Budget Performance Data

The government's budget performance is a benchmark for the government's success in optimizing people's money to achieve national goals. Even though performance measurement has reached the Work Unit level, the data formed still do not have a specific grouping, in the sense of unstructured data. The purpose of this research is to find the best clustering algorithm for classifying budget performance data. The data used is budget performance data for 19,460 Indonesian Government Work Units. The data is sourced from the SMART application and the OM SPAN application. This research uses a comparative study approach for the K-Means algorithm, DBSCAN, and agglomerative hierarchical clustering (AHC). Evaluation of the clustering results formed using the Davies-Bouldin Index (DBI) method. The AHC algorithm with k = 6 achieved the lowest DBI value of 0.3583472. The DBI value for the DBSCAN algorithm with MinPts = 10 is 0.5398259. However, the AHC algorithm is not good in terms of ease of implementation. Therefore, the K-means algorithm with parameters k = 10 is the best alternative. The K-Means algorithm gets a DBI value of 1.052678. The K-Means algorithm produces 10 clusters. Based on knowledge extraction, it is determined that cluster 2 and cluster 5 are ideal clusters in terms of budget performance. While the clusters that require attention are cluster 1, cluster 3, cluster 4, and cluster 8.

Estimating the efficacy of solar photovoltaic panels in Lebanon using a digital surface model: A geospatial approach

With the escalating need for alternative energy sources due to economic crises and fossil fuel shortages in Lebanon, solar photovoltaic (PV) panels have emerged as an attractive solution. This study examines the capacity and efficacy of rooftop-installed PV solar panels. Using geospatial technologies, including Digital Surface Models drone-based photogrammetry, the study assesses geometric and solar characteristics, seasonal solar radiation, solar duration, and power for 40 PV units installed in the study area. This research presents specific quantitative values for optimal orientations that result in high solar radiation across various seasons and identifies varying slopes influencing the performance of PV solar panels. Employing the Agglomerative Hierarchical Clustering (AHC) technique, PV units are systematically classified into clusters labeled as Moderate, High, Low, and Very Low solar power, offering quantitative metrics regarding the effectiveness of distinct panels. The high-efficiency Cluster exhibits an average solar power of 1868.114 kWh/m² during the summer season, whereas the Very Low Cluster, comprising panels with minimal solar power output, averages 150.578 kWh/m² in the same season. In conclusion, the most effective PV solar panels within the study area are those oriented between 195 and 225 degrees, with shallow inclination angles and larger surface areas contributing to enhanced performance in capturing solar radiation and generating power. These precise quantitative insights contribute to informed decision-making for optimizing the placement of PV panels to enhance energy generation. The study's recommendations are substantiated by specific numerical data, guiding future solar installations to maximize solar energy generation.

Evaluating visitors’ experience in museum: Comparing artificial intelligence and multi-partitioned analysis

Analysing visitors' behaviour in a museum or in a cultural site is a crucial element to manage spaces and artworks arrangement as well as improving the visit experience. This paper presents the preliminary results of the ARTEMISIA project, exploiting Artificial Intelligence (AI) techniques to study, design and develop a methodology to interpret visitors' behaviour within a museum context, namely the Museum of Rome in Palazzo Braschi (Rome, Italy). The aim is to combine literature on users' experience (UX) analysis with experimental data coming from the visitor anonymous tracking out of motion sensors (users' stand-still positions, viewpoint direction, movements), merging approaches of different research domains. Through the use of agglomerative hierarchical clustering algorithms, four categories of visitors were identified, then associated to user profiles emerged by UX evaluations. Such analysis may lead to new forms of visitors profiling and to the development of a new generation of customised applications in public and private contexts. Identifying and predicting users’ patterns with respect to museum halls arrangement may also be useful to suggest improvement in the museum spaces and exhibitions (new indications, updated storytelling or changes in thematic configuration).© 2023 Elsevier Ltd. All rights reserved.

Business Models of Ukrainian Banks: the Impact of the Revolution of Dignity, the COVID–19 Pandemic, and Russia’s Military Aggression

The purpose of the article is to identify key banking activity models in Ukraine and show how they changed under the influence of the Revolution of Dignity, the COVID–19 pandemic, and the imposition of martial law. The proposed method uses cluster analysis of the main indicators for banking activity (concerning assets, liabilities and income) based on Ward’s agglomerative hierarchical clustering algorithm and the Tau index in the NbClust package as the criterion for evaluating the optimal number of clusters. The research covers all Ukrainian banks, spanning the period 2013–2022. In 2014 (after the Revolution of Dignity), the actions of the National Bank of Ukraine (NBU) had a positive impact on the models of Ukrainian banks: there was no opaque model of banks and banking models became more transparent and more resilient to financial shocks. Between 2021 and 2022, five banking models were identified: universal banks, wholesale funding banks, corporate‑investment banks, retail banks, and commission banks. The negative impact of COVID–19 on the Ukrainian economy was reflected in all banking models by the following: (1) a significant increase in the role of securities in assets, which was caused by a decrease in lending due to an increase in their riskiness; (2) the use of central bank funds to liabilities management, which was evidence of a deterioration in financing conditions in the deposit market. The following main changes in Ukraine’s banking system at the end of 2022 (during the war) were identified: (1) a decrease in the number of banks that mainly use funds from the NBU to support their activities; (2) the closure of only four banks by the NBU (including two with majority shareholders from Russia) during the year; (3) an increase in the share of the non‑government debt securities portfolio in banks’ assets; (4) declines in ROE and ROA for all banking models during the war, and three banking models became unprofitable; (5) the significantly higher share of non‑performing loans in the credit portfolio for all banking models, especially for the retail banking model. The value‑added of this research is the analysis of how banking models in Ukraine have changed at the micro level (reflected in the financial statements) inter alia in wartime.

Weighted double Q-learning based eco-driving control for intelligent connected plug-in hybrid electric vehicle platoon with incorporation of driving style recognition

Driving style poses significant impacts on eco-driving performance of vehicles, especially for those with hybrid powertrains. By incorporating driving style recognition, an efficient velocity planning and energy management strategy is developed for a platoon of intelligent connected plug-in hybrid electric vehicles (PHEVs). Firstly, a high-fidelity driving style classification and recognition model is established based on the agglomerative hierarchical clustering algorithm, and then the support vector machine algorithm is employed to recognize the driving style. Next, a multi-objective velocity planning problem is formulated with the consideration of the fuel economy, driving adaptability and comfort, following performance, and driving safety optimization as the optimization objective, and is then solved based on orthogonal collocation direct transcription and the interior-point methods. Finally, an energy management strategy incorporating model predictive control and weighted double Q-learning algorithm is built. The simulation results demonstrated that the velocity planning algorithm incorporating driving styles achieved preferable adaptability and comfort. The proposed strategy can achieve up to most 98.88 % energy economy of the stochastic dynamic programming for the following PHEVs, and reduce the overall fuel consumption by 27.39 % for the platoon, comparing to that without driving style incorporation.

Robotic Multi-Boll Cotton Harvester System Integration and Performance Evaluation

Several studies on robotic cotton harvesters have designed their end-effectors and harvesting algorithms based on the approach of harvesting a single cotton boll at a time. These robotic cotton harvesting systems often have slow harvesting times per boll due to limited computational speed and the extended time taken by actuators to approach and retract for picking individual cotton bolls. This study modified the design of the previous version of the end-effector with the aim of improving the picking ratio and picking time per boll. This study designed and fabricated a pullback reel to pull the cotton plants backward while the rover harvested and moved down the row. Additionally, a YOLOv4 cotton detection model and hierarchical agglomerative clustering algorithm were implemented to detect cotton bolls and cluster them. A harvesting algorithm was then developed to harvest the cotton bolls in clusters. The modified end-effector, pullback reel, vacuum conveying system, cotton detection model, clustering algorithm, and straight-line path planning algorithm were integrated into a small red rover, and both lab and field tests were conducted. In lab tests, the robot achieved a picking ratio of 57.1% with an average picking time of 2.5 s per boll. In field tests, picking ratio was 56.0%, and it took an average of 3.0 s per boll. Although there was no improvement in the lab setting over the previous design, the robot’s field performance was significantly better, with a 16% higher picking ratio and a 46% reduction in picking time per boll compared to the previous end-effector version tested in 2022.

Mineralogical and elemental composition of the Middle Miocene coal seams from the Alpu coalfield (Eskişehir, Central Türkiye): Insights from syngenetic zeolite formation

This study focuses on determining mineralogical and elemental compositions of coal seams (to the upwards, D, C, B, A, and S0) within seven coal exploration wells from the Alpu coalfield (Eskişehir, Central Türkiye). Furthermore, the special goal of the study is a comparative analysis of the relations between the elements by using agglomerative hierarchical clustering algorithm with different linkage methods as well as different similarity measures. Clay minerals and quartz are commonly detected as abundant to dominant phases, while natural zeolite formations were detected in the studied seams C, B, A, and S0. The SEM-EDX data shows that clinoptilolites in zeolite minerals were observed within the organic matter, while crystalline and non-crystalline analcime minerals along with syngenetic authigenic rhomboid K-feldspars were only detected in the seam A from one studied well. The existence of some micron-sized minerals such as apatite, monazite, and Ti-oxides within the smectite matrix and the measurable amount of Ti in smectite imply that alteration of epiclastic and contemporaneous volcanic inputs was developed under weak acidic to neutral conditions during peat accumulation. The lack of natural zeolite and carbonate minerals in the seam D could be an indicator of weak acidic to neutral conditions and semi-closed hydrogeological conditions. Nevertheless, the alkalinity of mire water, water table, and hydrogeological regime seem to be variable during the accumulation of precursor peats of seams C, B, A, and S0. In turn, alteration of volcanic inputs was observed under neutral to weak alkaline conditions and semi-closed to closed hydrogeological regime. Hence, syngenetic authigenic, micron-sized clinoptilolites were formed. Moreover, the existence of authigenic rhomboid K-feldspars and syngenetic authigenic analcimes in certain exploration well could suggest local increases on dissolved Na+ concentrations, alkalinity, and water table. Except for volcanogenic origin for minerals, accessory micron-sized minerals, like chromite, pentlandite, and allanite grains presumably originated from clastic influxes of ophiolitic rocks in the basement into palaeomires. The variations in mire water chemistry and clastic-influx source area could also control the elemental enrichments in the studied seams. Epiclastic and contemporaneous volcanic inputs into palaeomires seem to control enrichments of Li, B, Sc, and Ti in coal samples, while clastic influx from ophiolitic rocks into palaeomires caused to enrichments of Cr, V and Ni. Furthermore, the liberated Ba, Sr, and As ions from the alteration of epiclastic and contemporaneous volcanic inputs are absorbed by syngenetic zeolite minerals, while anoxic conditions in the palaeomires resulted in precipitation of Sr-barite and As-bearing pyrite grains during peat accumulation and/or early diagenetic stages. Overall, the differences in water chemistry of mire water, epiclastic and contemporaneous volcanic inputs, and clastic influx from the adjacent areas also caused several elemental enrichments and variations in mineralogical compositions of the Middle Miocene coal seams in the Alpu coalfield.

TeraHAC: Hierarchical Agglomerative Clustering of Trillion-Edge Graphs

We introduce TeraHAC, a (1+ε)-approximate hierarchical agglomerative clustering (HAC) algorithm which scales to trillion-edge graphs. Our algorithm is based on a new approach to computing (1+ε)-approximate HAC, which is a novel combination of the nearest-neighbor chain algorithm and the notion of (1+ε)-approximate HAC. Our approach allows us to partition the graph among multiple machines and make significant progress in computing the clustering within each partition before any communication with other partitions is needed. We evaluate TeraHAC on a number of real-world and synthetic graphs of up to 8 trillion edges. We show that TeraHAC requires over 100x fewer rounds compared to previously known approaches for computing HAC. It is up to 8.3x faster than SCC, the state-of-the-art distributed algorithm for hierarchical clustering, while achieving 1.16x higher quality. In fact, TeraHAC essentially retains the quality of the celebrated HAC algorithm while significantly improving the running time.

Cardiometabolic multimorbidity and associated patterns of healthcare utilization and quality of life: Results from the Study on Global AGEing and Adult Health (SAGE) Wave 2 in Ghana.

Understanding the patterns of multimorbidity, defined as the co-occurrence of more than one chronic condition, is important for planning health system capacity and response. This study assessed the association of different cardiometabolic multimorbidity combinations with healthcare utilization and quality of life (QoL). Data were from the World Health Organization (WHO) study on global AGEing and adult health Wave 2 (2015) conducted in Ghana. We analysed the clustering of cardiometabolic diseases including angina, stroke, type 2 diabetes, and hypertension with unrelated conditions such as asthma, chronic lung disease, arthritis, cataract and depression. The clusters of adults with cardiometabolic multimorbidity were identified using latent class analysis and agglomerative hierarchical clustering algorithms. We used negative binomial regression to determine the association of multimorbidity combinations with outpatient visits. The association of multimorbidity clusters with hospitalization and QoL were assessed using multivariable logistic and linear regressions. Data from 3,128 adults aged over 50 years were analysed. We identified four distinct classes of multimorbidity: relatively "healthy class" with no multimorbidity (47.9%): abdominal obesity only (40.7%): cardiometabolic and arthritis class comprising participants with hypertension, type 2 diabetes, stroke, abdominal and general obesity, arthritis and cataract (5.7%); and cardiopulmonary and depression class including participants with angina, chronic lung disease, asthma, and depression (5.7%). Relative to the class with no multimorbidity, the cardiopulmonary and depression class was associated with a higher frequency of outpatient visits [β = 0.3; 95% CI 0.1 to 0.6] and higher odds of hospitalization [aOR = 1.9; 95% CI 1.0 to 3.7]. However, cardiometabolic and arthritis class was associated with a higher frequency of outpatient visits [β = 0.8; 95% CI 0.3 to 1.2] and not hospitalization [aOR = 1.1; 95% CI 0.5 to 2.9]. The mean QoL scores was lowest among participants in the cardiopulmonary and depression class [β = -4.8; 95% CI -7.3 to -2.3] followed by the cardiometabolic and arthritis class [β = -3.9; 95% CI -6.4 to -1.4]. Our findings show that cardiometabolic multimorbidity among older persons in Ghana cluster together in distinct patterns that differ in healthcare utilization. This evidence may be used in healthcare planning to optimize treatment and care.

Application of Cluster Analysis to Examine the Performance of Low-Cost Volatile Organic Compound Sensors

Airtight energy-efficient buildings of today need efficient ventilation to secure high indoor air quality. There is a need for affordable and reliable sensors to make demand control available in a broad range of ventilation systems. Low-cost metal oxide semiconductor (MOS) volatile organic compound (VOC) sensors offer such a possibility, but they are usually non-selective and react to broad range of compounds. The objective of the present paper was to use cluster analysis to assess the ability of five commercially available MOS VOC sensors to detect pollutants in a residential setting. We studied three scenarios: emissions from people (human bioeffluents), furnishing materials (linoleum), and human activity (surface cleaning with spray detergent). We monitored each scenario with five MOS VOC sensors and a proton-transfer-reaction–time-of-flight mass spectrometer (PTR-ToF-MS). We applied an agglomerative hierarchical clustering algorithm to evaluate the dissimilarity between clusters. Four of the five tested sensors produced signals in agreement with the concentration patterns measured with the PTR-ToF-MS; one sensor underperformed in all cases. Three sensors showed a very similar performance under all emission scenarios. The results showed that the clustering could help in understanding whether a particular sensor matched the intended emission scenario.

Cloud-Based Dynamic Task Scheduling and Load Balancing Using a Variety of Intelligent Schemes

One of the main problems with cloud computing is load balancing, which divides tasks into virtual machines (VMs) with varying lengths and completion times. As a result, keeping VMs loaded up during job scheduling and allocation is difficult. In this research, we suggested secure load balancing in a cloud context and resource-aware dynamic job scheduling utilizing intelligent algorithms. The process for the planned work is as follows: (1). The Human Mental Search (HMS) method is used to cluster Real-Time and Non-Real Time jobs into four categories: Real-Time and Short, Real-Time and Long, Non-Real Time and Short, and Non-Real Time and Long. (2). Knowledge of Energy and Delay Combined Tasks Tasks are planned based on task clustering, and the fuzzy VIKOR method is used to schedule them in the order of priority. (3). Security Risks aware VM Clustering, which employs the Hierarchical Agglomerative Clustering algorithm, consists of three distinct entities: a VM's information collector, which compiles data about VMs; a load monitor, which oversees the entire datacenter; and a decision maker, which assigns tasks by choosing the best VM. (4). Co-Resident assaults were stopped by the continuous-Actor-Critical-Algorithm (C- ACAL), which is used to pick the best VM. Using CloudSim, which measures performance in terms of response time, latency, resource utilization, throughput, energy consumption, and task execution success rate, the simulation is carried out.

Garment sizing system development for Amhara policemen uniforms using data mining techniques

Purpose Sizing system is a fundamental topic in garment fitting. The purpose of this study was to assess the fit of existing police uniforms (shirt, jacket, overcoat and trousers) and develop a sizing system for upper and lower body uniforms of Amhara policemen in Ethiopia. Design/methodology/approach In total, 35 body dimensions of 889 policemen were taken through a manual anthropometric survey following the procedures in ISO 8559:1989 after each subject was interviewed on issues related to garment fit. The anthropometric data were pre-processed, key body dimensions were identified by principal components analysis and body types were clustered by the agglomerative hierarchical clustering algorithm and verified by the XGBoost classifier in a Python programming environment. The developed size charts were validated statistically using aggregate loss and accommodation rate. Findings About 44% of the subjects encountered fit problems every time they own new readymade uniforms. Lengths and side seams of shirts, and lengths and waist girths of trousers are the most frequently altered garment sites. Analysis of the anthropometric measurements resulted in 13 and 15 sizes for the upper and lower bodies, respectively. Moreover, the comparison of the developed upper garment size chart with the existing size chart for a shirt showed a considerable difference. This indicates that inappropriate size charts create fit problems. Originality/value The study considers the analysis of fit problems and sizing system development in a less researched country. Moreover, the proposed data mining procedure and its application for size chart development is unique and workable.

Hierarchical Machine Learning of Low-Resolution Coarse-Grained Free Energy Potentials.

A force-matching-based method for supervised machine learning (ML) of coarse-grained (CG) free energy (FE) potentials─known as multiscale coarse-graining via force-matching (MSCG/FM)─is an efficient method to develop microscopically informed CG models that are thermodynamically and statistically equivalent to the reference microscopic models. For low-resolution models, when the coarse-graining is at supramolecular scales, objective-oriented clustering of nonbonded particles is required and the reduced description becomes a function of the clustering algorithm. In the present work, we explore the dependence of the ML of the CG Helmholtz FE potential on the clustering algorithm. We consider coarse-graining based on partitional (k-means, leading to Voronoi diagram) and hierarchical agglomerative (bottom-up) clustering algorithms common in unsupervised ML and develop theory connecting the MSCG/FM learned CG Helmholtz potential and the clustering statistics. By combining the agglomerative clustering and the MSCG/FM learning in a recursive manner, we propose an efficient ML methodology to develop the fine-to-low resolution hierarchies of the CG models. The methodology does not suffer from degrading accuracy or increased computational cost to construct larger hierarchies and as such does not impose an upper size limitation of the CG particles resulting from the extended hierarchies. The utility of the methodology is demonstrated by obtaining the bottom-up agglomerative hierarchy for liquid nitromethane from all-atom molecular dynamics (MD) simulations. For agglomerative hierarchies, we prove the existence of renormalization group transformations that indicate self-similarity and allow for learning the low-resolution MSCG/FM potentials at low computational cost by rescaling and renormalizing the certain finer-resolution members of the hierarchy. The hierarchies of the CG models can be used to carry out simulations under constant-pressure conditions.

An agglomerative hierarchical clustering approach to identify coexisting bacteria in groups of bacterial vaginosis patients

Polymicrobial syndromes such as Bacterial Vaginosis (BV), where there is a great diversity of microorganisms and causal connotations, turn it into a disease with complex dynamics in the bacteria’s coexistence in groups of patients. The main aim of this study was to explore a dataset of patients with BV to determine a more informed number of groups to create for further analysis of bacteria’s coexistence. The Agglomerative Hierarchical Clustering (AHC) algorithm was applied to a BV dataset from an urban population in southeastern Mexico consisting of 201 patient records with 59 patient attributes and three classes (BV-positive, BV-negative, BV-indeterminate). In the clustering results obtained, it is possible to identify different remarkable groups of patients. The most prevalent coexisting bacteria among patients with BV were Atopobium + Gardnerella vaginalis with 37.50%, Atopobium + Megasphaera with 15.68% in the first experiment. Whereas, in the second experiment, the coexisting bacteria were Atopobium + Megasphaera + Mycoplasma hominis with 33.33% and Atopobium + Gardnerella vaginalis + Mycoplasma hominis with 25%. Finally, we provided evidence that via the AHC algorithm, it was possible to identify an optimal number of clusters with high intra-similarity and inter-dissimilarity. Furthermore, this approach allowed us to create a clustering model that helps analyze the complex dynamics between bacteria in groups of patients with BV.

Time series cluster analysis reveals individual assignment of microbiota in captive tiger (Panthera tigris) and wildebeest (Connochaetes taurinus).

Fecal microbiota variability and individuality are well studied in humans and also in farm animals (related to diet- or disease-specific influences), but very little is known for exotic zoo-housed animals. This includes a wide range of species that differ greatly in microbiota composition and variation. For example, herbivorous species show a very similar and constant fecal microbiota over time, whereas carnivorous species appear to be highly variable in fecal microbial diversity and composition. Our objective was to determine whether species-specific and individual-specific clustering patterns were observed in the fecal microbiota of wildebeest (Connochaetes taurinus) and tigers (Panthera tigris). We collected 95 fecal samples of 11 animal individuals that were each sampled over eight consecutive days and analyzed those with Illumina MiSeq sequencing of the V3-V4 region of the 16SrRNA gene. In order to identify species or individual clusters, we applied two different agglomerative hierarchical clustering algorithms - a community detection algorithm and Ward's linkage. Our results showed that both, species-specific and individual-specific clustering is possible, but more reliable results were achieved when applying dynamic time warping which finds the optimal alignment between different time series. Furthermore, the bacterial families that distinguish individuals from each other in both species included daily occurring core bacteria (e.g., Acidaminococcaceae in wildebeests or Clostridiaceae in tigers) as well as individual dependent and more fluctuating bacterial families. Our results suggest that while it is necessary to consider multiple consecutive samples per individual, it is then possible to characterize individual abundance patterns in fecal microbiota in both herbivorous and carnivorous species. This would allow establishing individual microbiota profiles of animals housed in zoos, which is a basic prerequisite to quickly detect deviations and use microbiome analysis as a non-invasive and cost-effective tool in animal welfare.

Enhancing Spatial Debris Material Classifying through a Hierarchical Clustering-Fuzzy C-Means Integration Approach

This paper presents a novel approach for clustering spectral polarization data acquired from space debris using a fuzzy C-means (FCM) algorithm model based on hierarchical agglomerative clustering (HAC). The effectiveness of the proposed algorithm is verified using the Kosko subset measure formula. By extracting characteristic parameters representing spectral polarization from laboratory test data of space debris samples, a characteristic matrix for clustering is determined. The clustering algorithm’s parameters are determined through a random selection of points in the external field. The resulting algorithm is applied to pixel-level clustering processing of spectral polarization images, with the clustering results rendered in color. The experimental results on field spectral polarization images demonstrate a classification accuracy of 96.92% for six types of samples, highlighting the effectiveness of the proposed approach for space debris detection and identification. The innovation of this study lies in the combination of HAC and FCM algorithms, using the former for preliminary clustering, and providing a more stable initial state for the latter, thereby improving the effectiveness, adaptability, accuracy, and robustness of the algorithm. Overall, this work provides a promising foundation for space debris classification and other related applications.

Cavitation Diagnosis Method for Centrifugal Pumps based on Agglomerative Hierarchical Clustering Algorithm

Cavitation Diagnosis Method for Centrifugal Pumps based on Agglomerative Hierarchical Clustering Algorithm

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.

Classification of Blood Rheological Models through an Idealized Symmetrical Bifurcation

The assumed rheological behavior of blood influences the hemodynamic characteristics of numerical blood flow simulations. Until now, alternative rheological specifications have been utilized, with uncertain implications for the results obtained. This work aims to group sixteen blood rheological models in homogeneous clusters, by exploiting data generated from numerical simulations on an idealized symmetrical arterial bifurcation. Blood flow is assumed to be pulsatile and is simulated using a commercial finite volume solver. An appropriate mesh convergence study is performed, and all results are collected at three different time instants throughout the cardiac cycle: at peak systole, early diastole, and late diastole. Six hemodynamic variables are computed: the time average wall shear stress, oscillatory shear index, relative residence time, global and local non-Newtonian importance factor, and non-Newtonian effect factor. The resulting data are analyzed using hierarchical agglomerative clustering algorithms, which constitute typical unsupervised classification methods. Interestingly, the rheological models can be partitioned into three homogeneous groups, whereas three specifications appear as outliers which do not belong in any partition. Our findings suggest that models which are defined in a similar manner from a mathematical perspective may behave substantially differently in terms of the data they produce. On the other hand, models characterized by different mathematical formulations may belong to the same statistical group (cluster) and can thus be considered interchangeably.