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We consider the numerical taxonomy problem of fitting a positive distance function \({\mathcal {D}:{S\choose 2}\rightarrow \mathbb {R}_{\gt 0}}\) by a tree metric. We want a tree T with positive edge weights and including S among the vertices so that their distances in T match those in \(\mathcal {D}\). A nice application is in evolutionary biology where the tree T aims to approximate thebranching process leading to the observed distances in \(\mathcal {D}\) [Cavalli-Sforza and Edwards 1967]. We consider the total error, that is, the sum of distance errors over all pairs of points. We present a deterministic polynomial time algorithm minimizing the total error within a constant factor. We can do this both for general trees and for the special case of ultrametrics with a root having the same distance to all vertices in S. The problems are APX-hard, so a constant factor is the best we can hope for in polynomial time. The best previous approximation factor was O((log n)(log log n)) by Ailon and Charikar [2005], who wrote "determining whether an O(1) approximation can be obtained is a fascinating question." [ABSTRACT FROM AUTHOR]

Background: Trauma induced coagulopathy remains to be an important cause of high transfusion requirements and mortality and shock induced endotheliopathy (SHINE) has been implicated. Methods: European multicenter observational study of adult trauma patients with injury severity score ≥ 16 arriving within 2 h from injury to the trauma centers. Admission blood samples obtained were used for analysis of the SHINE biomarkers (syndecan-1, soluble thrombomodulin, adrenaline) and extensive analysis of coagulation, -and fibrinolytic factors together with collection of clinical data. Hierarchical clustering of the SHINE biomarkers was used to identify the SHINE phenotypes. Results: The 313 patients clustered into four SHINE phenotypes. Phenotype 2, having the highest glycocalyx shedding, encompassing 22% of the whole cohort, had severe coagulopathy with lower levels of prothrombin, FV, IX, X, XI and severe hyperfibrinolysis with higher plasmin – alpha 2-antiplasmin (PAP) – and tPA levels and lower alpha2 – antiplasmin levels. This phenotype had significantly higher transfusion requirements and higher mortality (39% vs. 23%, 15% and 14%) but similar injury severity score (ISS) compared to the others phenotypes. Conclusions: Hierarchical clustering identified four SHINE phenotype in a cohort of trauma patients. Trauma induced coagulopathy was confined to only one of the SHINE phenotypes, encompassing 22% of the total cohort. This phenotype was characterized by severe hypocoagulability and hyperfibrinolysis, which translated to significantly higher transfusion requirements and higher mortality compared to the other SHINE phenotypes with similar injury severity, warranting further investigation. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the change in mineral composition depending on tea variety, tea concentration, and steeping time. Four different tea varieties, black Ceylon (BC), black Turkish (BT), green Ceylon (GC), and green Turkish (GT), were used to produce teas at concentrations of 1, 2, and 3%, respectively. These teas were produced using 7 different steeping times: 2, 5, 10, 20, 30, 45, and 60 min. It was also aimed to optimize the regression equations utilizing these factors to identify parameters conducive to maximizing Zn, K, Cu, Mg, Ca, Na, and Fe levels; minimizing Al content, and maintaining Mn level at 5.3 mg/L. The optimal conditions for achieving a Mn content of 5.3 mg/L in black Turkish tea entailed steeping at a concentration of 1.94% for 11.4 min. Variations in K and Mg levels across teas were inconsistent with those observed for other minerals, whereas variations in Al, Cu, Fe, Mn, Na, and Zn levels exhibited a close relationship. Overall, mineral levels in tea can be predicted through regression analysis, and by mathematically optimizing the resultant equations, the requisite conditions for tea production can be determined to achieve maximum, minimum, or target mineral values. [ABSTRACT FROM AUTHOR]

The nonparametric bootstrap has been used in cluster analysis for various purposes. One of those purposes is to account for sampling variability. This can be achieved by obtaining a bootstrap approximation of the sampling distribution function of the estimator of interest and then clustering those distribution functions. Although the consistency of the nonparametric bootstrap in estimating transformations of the sample mean has been known for decades, little is known about how it carries over to clustering. Here, we investigated this problem with a simulation study. We considered single-linkage agglomerative hierarchical clustering and a three-type branching process for parametrized transformations of random vectors of relative frequencies of possible types of the index case of each process. In total, there were nine factors and 216 simulation scenarios in a fully-factorial design. The ability of the bootstrap-based clustering to recover the ground truth clusterings was quantified by the adjusted transfer distance between partitions. The results showed that in the best 18 scenarios, the average value of the distance was less than 20 percent of the maximum possible distance value. We noticed that the results most notably depended on the number of retained clusters, the distribution for sampling the prevalence of types, and the sample size appearing in the denominators of relative frequency types. The comparison of the bootstrap-based clustering results with so-called uninformed random partitioning results showed that in the vast majority of scenarios considered, the bootstrap-based approach led, on average, to remarkably lower classification errors than the random partitioning. [ABSTRACT FROM AUTHOR]

Hierarchical clustering is a widely used data analysis technique. Typically, tools for this method operate on data in its original, readable form, raising privacy concerns when a clustering task involving sensitive data that must remain confidential is outsourced to an external server. To address this issue, we developed a method that integrates Cheon-Kim-Kim-Song homomorphic encryption (HE), allowing the clustering process to be performed without revealing the raw data. In hierarchical clustering, the two nearest clusters are repeatedly merged until the desired number of clusters is reached. The proximity of clusters is evaluated using various metrics. In this study, we considered two well-known metrics: single linkage and complete linkage. Applying HE to these methods involves sorting encrypted distances, which is a resource-intensive operation. Therefore, we propose a cooperative approach in which the data owner aids the sorting process and shares a list of data positions with a computation server. Using this list, the server can determine the clustering of the data points. The proposed approach ensures secure hierarchical clustering using single and complete linkage methods without exposing the original data. [ABSTRACT FROM AUTHOR]

Introduction: Statistical shape modelling (SSM) is used to analyse morphology, discover qualitatively and quantitatively unique shape features within a population, and generate mean shapes and shape modes that show morphological variability. Hierarchical agglomerative clustering is a machine learning analysis used to identify subgroups within a given population in relation to shape features. We tested the application of both methods in the clinically relevant scenario of patients undergoing aortic valve repair (AVR). Every year, around 5000 patients undergo surgical AVR in the UK. Aims: Evaluate aortic morphology and identify subgroups amongst patients who had undergone AVR, including Ozaki, Ross, and valve-sparing procedures using SSM and unsupervised hierarchical clustering analysis. This methodological framework can evaluate both pre- and post-surgical variability across subgroups undergoing different surgeries. Methods: Pre- (n = 47) and post- (n = 35) operative three-dimensional (3D) aortic models were reconstructed from computed tomography (CT) and cardiac magnetic resonance (CMR) images. Computational analyses for SSM and hierarchical clustering were run separately for the two subgroups, assessing (a) ascending aorta only and (b) the whole aorta. This allows for exploring possible variations in morphological classification related to the input shape. Results: Most patients in the Ross procedure subgroup exhibited differences in aortic morphology from other subgroups, including an elongated ascending and wide aortic arch pre-operatively, and an elongated ascending aorta with a slightly enlarged sinus post-operatively. In hierarchical clustering, the Ross aortas also appeared to cluster together compared to the other surgical procedures, both pre-operatively and post-operatively. There were significant differences between clusters in terms of clustering distance in the pre-operative analyses (p = 0.003 for ascending aortas, p = 0.016 for whole aortas). There were no significant differences between the clusters in post-operative analyses (p = 0.47 for ascending, p = 0.19 for whole aorta). Conclusions: We demonstrated the feasibility of evaluating aortic morphology before and after different aortic valve surgeries using SSM and hierarchical clustering. This framework could be used to further explore shape features associated with surgical decision-making pre-operatively and, importantly, to identify subgroups whose morphology is associated with poorer clinical outcomes post-operatively. Statistical shape modelling (SSM) and unsupervised hierarchical clustering are two statistical methods that can be used to assess morphology, show morphological variations, with the latter being able to identify subgroups within a population. These methods have been applied to the population of aortic valve replacement (AVR) patients since there are different surgical procedures (traditional AVR, Ozaki, Ross, and valve-sparing). The aim is to evaluate aortic morphology and identify subgroups within this population before and after surgery. Computed tomography and cardiac magnetic resonance images were reconstructed into 3D models of the ascending aorta and whole aorta, which were then input into SSM and hierarchical clustering. The results show that the Ross aortic morphology is quite different from the other aortas. The clustering did not classify the aortas based on the surgical procedures; however, most of the Ross group did cluster together, indicating low variability within this surgical group. [ABSTRACT FROM AUTHOR]

Multimorbidity, defined as having 2 or more chronic conditions, is a growing public health concern, but research in this area is complicated by the fact that multimorbidity is a highly heterogenous outcome. Individuals in a sample may have a differing number and varied combinations of conditions. Clustering methods, such as unsupervised machine learning algorithms, may allow us to tease out the unique multimorbidity phenotypes. However, many clustering methods exist, and choosing which to use is challenging because we do not know the true underlying clusters. Here, we demonstrate the use of 3 individual algorithms (partition around medoids, hierarchical clustering, and probabilistic clustering) and a clustering ensemble approach (which pools different clustering approaches) to identify multimorbidity clusters in the AIDS Linked to the Intravenous Experience cohort study. We show how the clusters can be compared based on cluster quality, interpretability, and predictive ability. In practice, it is critical to compare the clustering results from multiple algorithms and to choose the approach that performs best in the domain(s) that aligns with plans to use the clusters in future analyses. [ABSTRACT FROM AUTHOR]

A growing literature suggests that gene expression can be greatly altered in disease conditions, and identifying those changes will improve the understanding of complex diseases such as cancers or diabetes. A prevailing direction in the analysis of gene expression studies the changes in gene pathways which include sets of related genes. Therefore, introducing structured exploration to differential analysis of gene expression networks may lead to meaningful discoveries. The topic of this paper is differential network analysis, which focuses on capturing the differences between two or more precision matrices. We discuss the connection between the thresholding method and the D-trace loss method on differential network analysis in the case that the precision matrices share the common connected components. Based on this connection, we further propose the cluster D-trace loss method which directly estimates the differential network and achieves model selection consistency. Simulation studies demonstrate its improved performance and computational efficiency. Finally, the usefulness of our proposed estimator is demonstrated by a real-data analysis on non-small cell lung cancer. [ABSTRACT FROM AUTHOR]

Green development evaluation is an important way for understanding the development status of a region. This study focuses on the green development evaluation of China's Yangtze River Economic Belt, which includes 11 provinces across the eastern, central, and western China. The population and gross domestic product of this area both exceed 40% of the national total. We first established a city-level green development evaluation index system, which includes indicators in the ecological, economic and social dimensions, based on complex ecosystem theory. There are two sub-dimensions under each dimension, and there are totally 21 indicators at the bottom layer. Then, we collected relevant data to measure the 21 indicators in three dimensions of the 110 cities in the Yangtze River Economic Belt area in 2017. Finally, the 110 cities were divided into 6 groups with hierarchical clustering. The green development level and characteristics of each group of cities were compared and analyzed. The results show that the green development levels of cities in China's Yangtze River Economic Belt are quite different and have different characteristics. In particular, some cities are facing dual pressures in the economy and the ecology. This study can support formulating more targeted green development policies and regional collaboration policies for the Yangtze River Economic Belt area. It also has important reference value for the green development evaluation of other regions. [ABSTRACT FROM AUTHOR]

Spectral clustering techniques depend on the eigenstructure of a similarity matrix to assign data points to clusters, so that points within the same cluster exhibit high similarity and are compared to those in different clusters. This work aimed to develop a spectral method that could be compared to clustering algorithms that represent the current state of the art. This investigation conceived a novel spectral clustering method, as well as five policies that guide its execution, based on spectral graph theory and embodying hierarchical clustering principles. Computational experiments comparing the proposed method with six state-of-the-art algorithms were undertaken in this study to evaluate the clustering methods under scrutiny. The assessment was performed using two evaluation metrics, specifically the adjusted Rand index, and modularity. The obtained results furnish compelling evidence, indicating that the proposed method is competitive and possesses distinctive properties compared to those elucidated in the existing literature. This suggests that our approach stands as a viable alternative, offering a robust choice within the spectrum of available same-purpose tools. [ABSTRACT FROM AUTHOR]

A new-fangled methodology for the appropriate location of solar PV in a primary distribution network for the enhancement of voltage profile and reduction of losses is presented in this paper. Since the power system network is very large, complex, and geographically diversified, the optimal location of solar PV is a very challenging task. This paper introduces a new performance index called Aggregate Loss Voltage Sensitivity Index (ALVSI), which is a combination of Loss Sensitivity Index (LSI) and Voltage Sensitivity Index (VSI) to address the above problem. The work carried out in this study is presented in two sections. The first section deals with the separation of Distribution Network (DN) into several zones using the distance matrix, which is calculated based on VSI and LSI. Multi-dimensional scaling (MDS) along with Hierarchical clustering, is employed for the partitioning of DN into zones. The second section concentrates on the allocation of multiple solar PV simultaneously in all the zones using a newly developed python coded Differential Evolution based ALVSI algorithm for the first time. The algorithm is tested on CIGRE Medium Voltage Distribution Network (CIGRE MV DN) and IEEE 69 bus radial distribution system and simulated in the Pandapower python module. The obtained results are compared with the existing literature results and found to be more promising and optimal. [ABSTRACT FROM AUTHOR]

Summary: A generic variation of hierarchical clustering (HC) that builds clusters from the bottom up is called agglomerative hierarchical clustering (AHC). The extension of AHC techniques using similarity criteria is the main topic of this research. Based on this, we create an AHC method that accomplishes clustering through ensemble approaches and combines the clustering of clusters with an original similarity measure. Three steps make up the proposed method's primary section. The first phase combines several individual AHC techniques to identify links between samples and create preliminary clusters. A heuristic similarity measure based on the developed clusters is used to determine how similar the samples are. The initial clusters produced using various techniques are all re‐clustered to create superclusters in the second step. The third phase involves creating the final clusters by assigning each sample to a supercluster with the greatest similarity after the clusters have been formed. Based on several benchmark datasets from the UCI machine learning repository, extensive experimental research has been done to assess the performance of the suggested approach. The outcomes unequivocally demonstrate that the suggested AHC‐based paradigm outperforms cutting‐edge techniques. [ABSTRACT FROM AUTHOR]

By constructing a complex network model grounded in time series analysis, this study delves into the intricate relationships between the stock markets of 18 countries worldwide. Utilizing 31 distinct time series distance functions to formulate the network, we employ Hamming distance to quantify the resemblance between networks derived from different distance functions. By modulating the network density through distance percentiles ( p = 0.1 , 0.3, 0.5), we demonstrate the similarity of various distance functions across multiple density levels. Our findings reveal that certain distance functions exhibit high degrees of similarity across varying network densities, suggesting their potential for mutual substitution in network construction. Furthermore, the centroid network identified via hierarchical cluster analysis highlights the similarities between the stock markets of different nations, mirroring the intricate interconnections within the global financial landscape. The insights gained from this study offer crucial perspectives for comprehending the intricate network structure of global financial time series data, paving the way for further analysis and prediction of global financial market dynamics. [ABSTRACT FROM AUTHOR]

The rapid evolution of computer technology and social networks has led to massive data generation through interpersonal communications, necessitating improved methods for information mining and relational analysis in areas such as criminal activity. This paper introduces a Social Network Forensic Analysis model that employs network representation learning to identify and analyze key figures within criminal networks, including leadership structures. The model incorporates traditional web forensics and community algorithms, utilizing concepts such as centrality and similarity measures and integrating the Deepwalk, Line, and Node2vec algorithms to map criminal networks into vector spaces. This maintains node features and structural information that are crucial for the relational analysis. The model refines node relationships through modified random walk sampling, using BFS and DFS, and employs a Continuous Bag-of-Words with Hierarchical Softmax for node vectorization, optimizing the value distribution via the Huffman tree. Hierarchical clustering and distance measures (cosine and Euclidean) were used to identify the key nodes and establish a hierarchy of influence. The findings demonstrate the effectiveness of the model in accurately vectorizing nodes, enhancing inter-node relationship precision, and optimizing clustering, thereby advancing the tools for combating complex criminal networks. [ABSTRACT FROM AUTHOR]

Climate classification plays a fundamental role in understanding climatic patterns, particularly in the context of a changing climate. This study utilized hourly meteorological data from 36 major cities in China from 2011 to 2021, including 2 m temperature (T2), relative humidity (RH), and precipitation (PRE). Both original hourly sequences and daily value sequences were used as inputs, applying two non-hierarchical clustering methods (k-means and k-medoids) and four hierarchical clustering methods (ward, complete, average, and single) for clustering. The classification results were compared using two clustering evaluation indices: the silhouette coefficient and the Calinski–Harabasz index. Additionally, the clustering was compared with the Köppen–Geiger climate classification based on the maximum difference in intra-cluster variables. The results showed that the clustering method outperformed the Köppen–Geiger climate classification, with the k-medoids method achieving the best results. Our research also compared the effectiveness of climate classification using two variables (T2 and PRE) versus three variables, including the addition of hourly RH. Cluster evaluation confirmed that incorporating the original sequence of hourly T2, PRE, and RH yielded the best performance in climate classification. This suggests that considering more meteorological variables and using hourly observation data can significantly improve the accuracy and reliability of climate classification. In addition, by setting the class numbers to two, the clustering methods effectively identified climate boundaries between northern and southern China, aligning with China's traditional geographical division along the Qinling–Huaihe River line. [ABSTRACT FROM AUTHOR]

This manuscript proposes Strategic Improved K-Means Clustering to simplify blood donor data analysis and distribution. The technique optimizes blood donor system resources via K-Means++ initialization, hierarchical clustering, and smart data dissemination. The paper begins with a comprehensive overview of clustering techniques and their healthcare applications. It illustrates the need for contemporary blood donor data analysis methods for cluster quality and resource allocation. Cluster purity, silhouette coefficient, Davies-Bould in the index, and other performance indicators are used to rigorously compare the recommended technique to 10 established clustering methods. The approach routinely fulfils these conditions, proving that it creates accurate, well-fitting groupings. Ablation tests how much-enhanced initialization, hierarchical clustering, and strategic data placement improve the entire. The study found that these make the procedure dependable and successful for numerous sorts of data. The study shows that the approach may be applied to other data besides blood donor data. Hierarchical clustering provides important information about the dataset's hierarchical patterns, making clustering findings easier to grasp. Resources are better distributed with strategic data dissemination. The recommended strategy is effective in emergencies and areas with changing blood needs. To conclude, Strategic Improved K-Means Clustering evaluates and distributes blood donor data comprehensively. Its flexibility, adaptability, and speed make it excellent for managing healthcare resources and making collective choices. [ABSTRACT FROM AUTHOR]

Beans usually have similar physical attributes; thus, it is difficult to distinguish them manually. Size, shape, and mass attributes of seeds help in breeding, selection, classification, separation, and machine design. This study was conducted to determine physical attributes of 20 bean genotypes with the use of image processing techniques. Color characteristics of the present genotypes were also determined. Then, four different machine learning algorithms (MLP, RF, SVR, and k-NN) were employed to predict seed mass. Among the present genotypes, Güzelöz and Özdemir genotypes had the highest size, shape, and color characteristics. Highly significant positive correlations were encountered between projected area-equivalent diameter (r = 1.00), between geometric mean diameter—surface area and volume (r = 1.00). On the other hand, highly significant negative correlations were seen between sphericity—elongation in vertical orientation (r = − 0.98). In hierarchical cluster analysis for physical attributes, Alberto–Aslan and Aras 98–Şahin genotypes were identified as the closest genotypes. According to PCA analysis, the first two principal components (PC1 and PC2) were able to explain 73% of total variation among the genotypes. While PC1 axis included projected area (vertical), equivalent diameter (vertical), and length, PC2 axis included L*, a*, b*, sphericity, roundness (vertical), and elongation (vertical). Among the present machine learning algorithms, RF yielded the best performances in mass estimation of bean seeds. It was concluded that machine learning techniques increased the efficiency of related machinery and helped to save time and labor. [ABSTRACT FROM AUTHOR]

Graph summarization is a useful tool for analyzing large-scale graphs. Some works tried to preserve original node embeddings encoding rich structural information of nodes on the summary graph. However, their algorithms are designed heuristically and not theoretically guaranteed. In this article, we theoretically study the problem of preserving node embeddings on summary graph. We prove that three matrix-factorization-based node embedding methods of the original graph can be approximated by that of the summary graph, and we propose a novel graph summarization method, named HCSumm, based on this analysis. Extensive experiments are performed on real-world datasets to evaluate the effectiveness of our proposed method. The experimental results show that our method outperforms the state-of-the-art methods in preserving node embeddings. [ABSTRACT FROM AUTHOR]

Pharmaceutical researchers are continually searching for techniques to improve both drug development processes and patient outcomes. An area of recent interest is the potential for machine learning (ML) applications within pharmacology. One such application not yet given close study is the unsupervised clustering of plasma concentration-time curves, hereafter, pharmacokinetic (PK) curves. In this paper, we present our findings on how to cluster PK curves by their similarity. Specifically, we find clustering to be effective at identifying similar-shaped PK curves and informative for understanding patterns within each cluster of PK curves. Because PK curves are time series data objects, our approach utilizes the extensive body of research related to the clustering of time series data as a starting point. As such, we examine many dissimilarity measures between time series data objects to find those most suitable for PK curves. We identify Euclidean distance as generally most appropriate for clustering PK curves, and we further show that dynamic time warping, Fréchet, and structure-based measures of dissimilarity like correlation may produce unexpected results. As an illustration, we apply these methods in a case study with 250 PK curves used in a previous pharmacogenomic study. Our case study finds that an unsupervised ML clustering with Euclidean distance, without any subject genetic information, is able to independently validate the same conclusions as the reference pharmacogenomic results. To our knowledge, this is the first such demonstration. Further, the case study demonstrates how the clustering of PK curves may generate insights that could be difficult to perceive solely with population level summary statistics of PK metrics. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusion Comorbidities in primary care do not occur in isolation but tend to cluster together causing various clinically complex phenotypes. This study aimed to distinguish phenotype clusters and identify the risks of all‐cause mortality in primary care.The baseline cohort of the LIPIDOGEN2015 sub‐study involved 1779 patients recruited by 438 primary care physicians. To identify different phenotype clusters, we used hierarchical clustering and investigated differences between clinical characteristics and mortality between clusters. We then performed causal analyses using causal mediation analysis to explore potential mediators between different clusters and all‐cause mortality.A total of 1756 patients were included (mean age 51.2, SD 13.0; 60.3% female), with a median follow‐up of 5.7 years. Three clusters were identified: Cluster 1 (n = 543) was characterised by overweight/obesity (body mass index ≥ 25 kg/m2), older (age ≥ 65 years), more comorbidities; Cluster 2 (n = 459) was characterised by non‐overweight/obesity, younger, fewer comorbidities; Cluster 3 (n = 754) was characterised by overweight/obesity, younger, fewer comorbidities. Adjusted Cox regression showed that compared with Cluster 2, Cluster 1 had a significantly higher risk of all‐cause mortality (HR 3.87, 95% CI: 1.24–15.91), whereas this was insignificantly different for Cluster 3. Causal mediation analyses showed that decreased protein thiol groups mediated the hazard effect of all‐cause mortality in Cluster 1 compared with Cluster 2, but not between Clusters 1 and 3.Overweight/obesity older patients with more comorbidities had the highest risk of long‐term all‐cause mortality, and in the young group population overweight/obesity insignificantly increased the risk in the long‐term follow‐up, providing a basis for stratified phenotypic risk management. [ABSTRACT FROM AUTHOR]

Spike sorting, an indispensable process in the analysis of neural biosignals, aims to segregate individual action potentials from mixed recordings. This study delves into a comprehensive investigation of diverse unsupervised classification algorithms, some of which, to the best of our knowledge, have not previously been used for spike sorting. The methods encompass Principal Component Analysis (PCA), K-means, Self-Organizing Maps (SOMs), and hierarchical clustering. The research draws insights from both macaque monkey and human pancreatic signals, providing a holistic evaluation across species. Our research has focused on the utilization of the aforementioned methods for the sorting of 327 detected spikes within an in vivo signal of a macaque monkey, as well as 386 detected spikes within an in vitro signal of a human pancreas. This classification process was carried out by extracting statistical features from these spikes. We initiated our analysis with K-means, employing both unmodified and normalized versions of the features. To enhance the performance of this algorithm, we also employed Principal Component Analysis (PCA) to reduce the dimensionality of the data, thereby leading to more distinct groupings as identified by the K-means algorithm. Furthermore, two additional techniques, namely hierarchical clustering and Self-Organizing Maps, have also undergone exploration and have demonstrated favorable outcomes for both signal types. Across all scenarios, a consistent observation emerged: the identification of six distinctive groups of spikes, each characterized by distinct shapes, within both signal sets. In this regard, we meticulously present and thoroughly analyze the experimental outcomes yielded by each of the employed algorithms. This comprehensive presentation and discussion encapsulate the nuances, patterns, and insights uncovered by these algorithms across our data. By delving into the specifics of these results, we aim to provide a nuanced understanding of the efficacy and performance of each algorithm in the context of spike sorting. [ABSTRACT FROM AUTHOR]

Understanding the trend of seasonal rainfall in the context of climate change is crucial for the maintenance of regional water resources management. The present study examines the seasonal rainfall trend in the Mahi River basin, India by using the wavelet transform and clustering method. Daily gridded rainfall data (0.25° × 0.25° spatial resolution) for 51 grids from 1970 to 2020 have been taken from India Meteorological Department (IMD). The monthly, seasonal, and annual statistic has been analyzed for 51 years. We have also plotted the relationship between precipitation trend–elevation. After that, homogeneous precipitation regions are delineated with hierarchical clustering analysis. Results reveal that seasonal precipitation over the basin clusters into 4 subregions for monsoon and 3 subregions for winter, pre-monsoon, and post-monsoon seasons. After the regionalization of the subregions, the periodicity and the inter-seasonal relationship were analyzed using continuous wavelet transform (CWT). In addition, it was clear that the cross-correlation between pre-monsoon and monsoon seasons had a significant periodic change in the last 30 years over the basin. The basin is located adjacent to the Arabian sea, which makes the basin more sensitive to a natural event like ENSO, it can cause changes in ocean temperatures and atmospheric circulation patterns that affect precipitation patterns during the study period. It is expected that some natural phenomena like monsoon variability, low-level jet streams, and many more also have an impact on the rainfall patterns of the basin. Thus, the understanding of seasonal precipitation variation provides a practical reference for water resources management, agricultural planning, and a forecast of precipitation in different regions and river basins of India which may give a better climate change indication. [ABSTRACT FROM AUTHOR]

The vast and unstructured data on social media platforms offer insights into user-perceived quality, presenting a novel avenue for new energy vehicle companies to analyze product quality. In response, this study introduces a methodology that integrates dependency syntactic parsing with hierarchical clustering to derive multi-level insights on user-perceived quality. Initially, we employ dependency syntactic parsing and part-of-speech tagging to identify compound noun phrases within comments. These phrases serve as a specialized out-of-vocabulary library pertinent to the new energy vehicle sector, from which a selection of words is chosen as potential evaluation metrics. Subsequently, we utilize Word2Vec to develop word vectors from automotive forum corpora. Leveraging these word vectors and the identified evaluation metrics, a hierarchical clustering algorithm is then applied to establish a comprehensive three-level user-perceived quality indicator system. Experimental validation conducted on forum comment data from BYD's new energy vehicles confirms the reliability and effectiveness of the proposed methodology. The user-perceived quality extraction method delineated in this research aids automotive firms in pinpointing areas of user interest, thereby substantially enhancing user loyalty and satisfaction. [ABSTRACT FROM AUTHOR]

Objective: Mezcal agaves in the state of Oaxaca have a high economic value due to the demand for mezcal production; therefore, the purpose of this work was to assess and highlight the importance of morphological diversity within and among cultivated Agave species. Design/Methodology/Approach: A completely randomized experimental design with 25 treatments (populations) and 11 replicates (individuals) was implemented. The plant (individual) was the experimental unit and 19 morphological descriptors proposed by the National Seed Inspection and Certification Service (SNICS) were assessed. A total of 275 individuals of the Agave angustifolia, Agave karwinskii, Agave marmorata, Agave rhodacantha, Agave potatorum, Agave seemanniana, and Agave nussaviorum species were assessed using a multivariate analysis to determine their phenotypic variability and existing relationships. Results: The dendrograms for the Q-mode and R-mode were obtained by means of a cluster analysis, forming 4 groups based on the average linkage generated from the standardized BDM of the sampled species of the genus Agave. Four groups were formed using the k-means clustering method, in accordance with field observations and a review of the taxonomic bibliography. The first two principal components (PC) accounted for 66.4% of the total variation, according to the principal component analysis (PCA). For PC1 and PC2, the variables with the highest contribution were those related to leaf shape (Fh), size of the lateral spine (FEL), number of leaves (Nh), plant height (H), uniformity in the size of the lateral spines (UTE), and terminal spine shape (FET). Study Limitations/Implications: A comprehensive study requires taxonomic keys to identify species, subspecies, and even varieties of Agave. Additionally, molecular characterization is essential to understand the variability and phylogenetic relationships of these populations, subject to a phylogenetic analysis. Findings/Conclusions: Multivariate analysis techniques revealed that three species showed high phenotypic variability in the maturation stage under cultivation conditions. The A. potatorum and A. karwinskii species had a greater intra-population phenotypic variability, with significant differences within the same species. Agave marmorata showed no intra- or inter-population variability. Leaf texture (Txh) was the only variable that explained the variation within its group. This is a tall species whose diameter is larger than in the other species. The variables of the Mexican (Agave rhodacantha) group showed low correlation, as their behavior was highly dispersed. The variables obtained in the field from this group of populations must be meticulously assessed to identify the degree of correlation between the variables and to confirm the behavior of this group. [ABSTRACT FROM AUTHOR]

This article describes a range of high‐dimensional data visualization strategies that we have explored for their ability to complement machine learning algorithm predictions derived from MultiFlow® assay results. For this exercise, we focused on seven biomarker responses resulting from the exposure of TK6 cells to each of 126 diverse chemicals over a range of concentrations. Obviously, challenges associated with visualizing seven biomarker responses were further complicated whenever there was a desire to represent the entire 126 chemical data set as opposed to results from a single chemical. Scatter plots, spider plots, parallel coordinate plots, hierarchical clustering, principal component analysis, toxicological prioritization index, multidimensional scaling, t‐distributed stochastic neighbor embedding, and uniform manifold approximation and projection are each considered in turn. Our report provides a comparative analysis of these techniques. In an era where multiplexed assays and machine learning algorithms are becoming the norm, stakeholders should find some of these visualization strategies useful for efficiently and effectively interpreting their high‐dimensional data. [ABSTRACT FROM AUTHOR]

Loss of hand functions, often manifesting in the form of weakness or spasticity from conditions like stroke or multiple sclerosis, poses challenges in performing activities of daily living (ADLs). The broad area of rehabilitation robotics provides the tools and knowledge necessary for implementing efficient restorative therapies. These therapies aim to improve hand functionality with minimal therapist intervention. However, the human hand evolved for various precision and power gripping tasks, with its intricate anatomy featuring a large number of degrees of freedom—up to 31—which hinder its modeling in many rehabilitation scenarios. In the process of designing prosthetic devices, instrumented gloves, and rehabilitation devices, there is a clear need to obtain simplified rehabilitation-oriented hand models without compromising their representativeness across the population. This is where the concept of kinematic reduction, focusing on specific grasps, becomes essential. Thus, the objective of this study is to uncover the intra-finger dependencies during finger flexion/extension by analyzing a comprehensive database containing recorded trajectories for 23 different functional movements related to ADLs, involving 77 test subjects. The initial phase involves data wrangling, followed by correlation analysis aimed at selecting 116 dependency-movement relationships across all grasps. A regularized generalized linear model is then applied to select uncorrelated predictors, while a linear mixed-effect model, with reductions based on both predictor significance and effect size, is used for modeling the dependencies. As a final step, agglomerative clustering of models is performed to further facilitate flexibility in tradeoffs in hand model accuracy/reduction, allowing the modeling of finger flexion extensions using 5–15 degrees of freedom only. [ABSTRACT FROM AUTHOR]

The current lack of harmonized standard test conditions for marine shipping hinders the comparison of performance and compliance assessments for different types of ships. This article puts forward a method for extracting ship loading cycles using machine learning algorithms. Time-series data are extracted from real ships in operation, and a segmented linear approximation method and a data normalization technique are adopted. A hierarchical-clustering type of soft dynamic time-warping similarity analysis method is presented to efficiently analyze the similarity of different time-series data, using soft dynamic time warping (Soft-DTW) combined with hierarchical clustering algorithms from the field of machine learning. The problem of data bias caused by spatial and temporal offset characteristics is effectively solved in marine test condition data. The validity and reliability of the proposed method are validated through the analysis of case data. The results demonstrate that the hierarchically clustered soft dynamic time-warping similarity analysis method can be considered reliable for obtaining test cases with different characteristics. Furthermore, it provides input conditions for effectively identifying the operating conditions of different types of ships with high levels of energy consumption and high emissions, thus allowing for the establishment of energy-saving and emissions-reducing sailing strategies. [ABSTRACT FROM AUTHOR]

Due to the high heterogeneity of cancers, it is rather essential to explore driver modules with the help of gene mutation data as well as known interactions between genes/proteins. Unfortunately, latent false positive interactions are inevitable in the Protein-Protein Interaction (PPI) network. Hence in the presented method, a new weight evaluation index, based on the gene-microRNA network as well as somatic mutation profile, is introduced for weighting the PPI network first. Subsequently, the vertices in the weighted PPI network are hierarchically clustered by measuring the Mahalanobis distance of their feature vectors, extracted with the graph embedding method Node2vec. Finally, a heuristic process with dropping and extracting is conducted on the gene clusters to produce a group of gene modules. Numerous experiment results demonstrate that the proposed method exhibits superior performance to four cutting-edge identification methods in most cases regarding the capability of recognizing the acknowledged cancer-related genes, generating modules having relatively high coverage and mutual exclusivity, and are significantly enriched for specific types of cancers. The majority of the genes in the identified modules are involved in cancer-related signaling pathways, or have been reported to be carcinogenic in the literature. Furthermore, many cancer related genes detected by the proposed method are actually omitted by the four comparison methods, which has been verified in the experiments. [ABSTRACT FROM AUTHOR]

Abstract Background Trauma induced coagulopathy remains to be an important cause of high transfusion requirements and mortality and shock induced endotheliopathy (SHINE) has been implicated. Methods European multicenter observational study of adult trauma patients with injury severity score ≥ 16 arriving within 2 h from injury to the trauma centers. Admission blood samples obtained were used for analysis of the SHINE biomarkers (syndecan-1, soluble thrombomodulin, adrenaline) and extensive analysis of coagulation, -and fibrinolytic factors together with collection of clinical data. Hierarchical clustering of the SHINE biomarkers was used to identify the SHINE phenotypes. Results The 313 patients clustered into four SHINE phenotypes. Phenotype 2, having the highest glycocalyx shedding, encompassing 22% of the whole cohort, had severe coagulopathy with lower levels of prothrombin, FV, IX, X, XI and severe hyperfibrinolysis with higher plasmin – alpha 2-antiplasmin (PAP) – and tPA levels and lower alpha2 – antiplasmin levels. This phenotype had significantly higher transfusion requirements and higher mortality (39% vs. 23%, 15% and 14%) but similar injury severity score (ISS) compared to the others phenotypes. Conclusions Hierarchical clustering identified four SHINE phenotype in a cohort of trauma patients. Trauma induced coagulopathy was confined to only one of the SHINE phenotypes, encompassing 22% of the total cohort. This phenotype was characterized by severe hypocoagulability and hyperfibrinolysis, which translated to significantly higher transfusion requirements and higher mortality compared to the other SHINE phenotypes with similar injury severity, warranting further investigation.

Abstract This paper processes a novel hierarchical high-dimensional clustering algorithm based on the Active Learning Method (ALM), which is a fuzzy-learning algorithm. The hierarchical part of the algorithm is composed of two phases: divisible and agglomerative. The divisible phase, a zooming-in-process, searches for sub-clusters in already-found clusters hierarchically. At each level of the hierarchy, the clusters are found by an ensemble clustering method based on the density of data. This part of the algorithm blurs each data point as multiple one-dimensional fuzzy membership functions called ink-drop patterns; then, it accumulates the ink-drop patterns of all data points on every dimension separately. Next, it performs one-dimensional density partitioning to produce an ensemble of clustering solutions; after that, combining the results is done based on a novel consensus method with the aid of prime numbers. An agglomerative phase is a bottom-up approach that merges clusters based on a novel distance metric, named $${K}^{2}$$ K 2 -nearest neighbor. The algorithm is named as the Hierarchical High-Dimensional Unsupervised Active Learning Method (HiDUALM) and is explained in more detail throughout this paper. Although the classical clustering methods are not suitable for high-dimensional data clustering, the proposed method solves the problems related to speed and memory using ensemble learning, while due to its hierarchy and the use of different distance criteria, different levels of the cluster provide the clause. Experiments on synthetic and real-world datasets are presented to show the effectiveness of the proposed-clustering algorithm.

Despite the economic and societal benefits of digitalisation and digital transformation, it is necessary to map country's digital conditions and identify the digital divide to formulate an effective strategy. The digital divide should be measured periodically to monitor progress and determine continuous improvement. This paper identifies the current digital divide among provinces in Indonesia. The study uses the hierarchical agglomerative clustering method based on The Indonesian Digital Society Index data from the Ministry of Communication and Informatics. It also analyses some key factors of the digital divide based on data from the Indonesian Bureau of Statistics using the multiple linear regression model. The results show three types of the digital divide across Indonesian provinces related to access, usage, and outcomes of information and communication technology. Gross Regional Domestic Product per capita, Wage/Salary, Proportion of Formal Labor, and Size of the Working-Age Population are identified as factors significantly affecting the digital divide.

Clustering is a complex data mining tool, useful to identify similarities in large amount of data, the medical databases being highly suitable in this regard. Our paper aims to compare the efficacy of two well-known clustering methods, the n-means algorithm and the classical hierarchical algorithm, and to apply them in analyzing a medical-economic database on dietary habits, social economic status and oral health in a sample of 326 men, aged between 25 and 30, living in the urban area – in order to identify possible associations between dietary habits and income levels. We identified 4 clusters which correspond partially to the 4 income levels recorded in the investigated sample and reveal the associated dietary habits. The n-means clustering performed better than the Single Linkage hierarchical classification, being therefore highly suitable in the analysis of socio-economic and general health data.

The aquaculture industry in China is not complete without the freshwater industry. The scientific assessment of the regional competitiveness of the freshwater aquaculture industry has significant theoretical and practical implications for fostering the industry's sustainable development. Based on the diamond model, this study developed a competitiveness evaluation index system for Chinese freshwater aquaculture industry which included six primary indicators and sixteen secondary indicators. The primary indicators included resource endowment, production capacity, science and technology promotion capacity, related and supporting industries, market demand and guiding development policy. The study analyzed freshwater aquaculture industrial competitiveness during 2012–2020 and classified the Chinese 18 major freshwater aquaculture provinces into five competitive levels. The results showed that the development of related and supporting industries has the greatest contribution to the competitiveness, followed by resource endowment and market demand capacity. The analysis and tier classification showed that the competitiveness of Jiangsu Province and Hubei Province has always been at the forefront, and the province of Guangdong, Fujian, Hunan, Jiangxi, Zhejiang, Anhui, Liaoning, and Guangxi were above the middle level. Moreover, the results indicated that the competitiveness of most provinces showed an upward trend.