Data Clustering Research Articles

How nurse researchers can use stepped-wedge design and analysis.

Designing effective health interventions and evaluating their impact is crucial to improving the health of the population. To ensure interventions are of high quality and effective, evidence-based research is essential, particularly studies that use randomised controlled trials (RCTs) or systematic reviews. However, RCTs may not be feasible or ethical in certain situations, such as in intensive care units. Cluster or stepped-wedge RCTs are alternative ways to assess interventions that also address these ethical concerns. To explain the stepped-wedge design and its main features as well as how to use it to evaluate nursing interventions. Understanding stepped-wedge designs empowers nurses to implement evidence-based interventions and improve patient outcomes. The use of stepped-wedge designs has increased in nursing research over the past two decades, indicating growing recognition of its advantages: efficient evaluation of healthcare interventions, ensuring all clusters receive treatment over time; smaller sample sizes; ethical considerations; and time control. However, challenges remain: ensuring nurse researchers' understanding and application of it is consistent, extended duration and logistical complexities. Methodological rigour, collaboration and understanding of secular trends are crucial, and nurses' involvement in RCTs enhances cluster selection, data collection and dissemination. The stepped-wedge design offers an ethical and adaptable method for studying interventions, considering healthcare complexities and allocating resources. Its versatility assists the advancement of nursing care delivery and in promoting evidence-based practice. Understanding stepped-wedge designs in nursing practice enhances evidence-based care, decision-making, collaboration and professional development, benefiting patient outcomes.

Convex Optimization Techniques for High-Dimensional Data Clustering Analysis: A Review

Convex Optimization Techniques for High-Dimensional Data Clustering Analysis: A Review

Radial Basis Function Neural Network with ensemble clustering for modeling mathematics achievement in Indonesia based on cognitive and non-cognitive factors

Mathematics achievement could be influenced by cognitive and non-cognitive factors. The potential variable of cognitive factor is metacognition, whereas non-cognitive factors include Economic, Social, and Cultural Status (ESCS), resilience, life satisfaction, happiness, pride, fear, sadness, and gender. Those variables involve numerical and categorical data. For this reason, this study aims to apply the Radial Basis Function Neural Network (RBFNN) model with ensemble clustering to model the relation between cognitive and non-cognitive aspects and mathematics achievement. The RBFNN is a soft computing approach based on the neural network model and has been shown as an effective model and free of assumption. The ensemble clustering is a process in RBFNN modeling to capture the independent variables involving the numerical and categorical data. It employs K-means clustering for the numerical data and K-modes for categorical data and combines the results of those two methods. The data used in this study are published by PISA (Program for International Student Assessment) 2018. The results show that the RBFNN with ensemble clustering deliver good performance in modeling the students’ mathematics achievement based on the cognitive and non-cognitive factors in terms of prediction accuracy. Other than RBFNN model, the use of cognitive and non-cognitive factors involving in this study also contributes to the high accuracy prediction. This further emphasizes that these factors are good predictors of mathematic achievement. Additionally, we suggest the silhouette cluster validation in the clustering process, since it leads to the number of hidden neurons of the best RBFNN model.

Multi-view clustering with semantic fusion and contrastive learning

Multi-view data obtained from different sources provides a comprehensive way to model the real world. As one of the fundamental analyses, clustering for multi-view data has attracted increasing attention from researchers in different fields. Although existing multi-view clustering methods have achieved promising results, how to fully utilize the consensus and differences among different views and how to integrate their features to learn a unified representation is still challenging. To overcome the limitations, we put forward a Semantic fusion and Contrastive learning model for Multi-View Clustering (SCMVC) in this paper. SCMVC employs the view-specific encoders to extract embeddings from each view, and reconstructs the topological and attribute information by using the corresponding decoders. Simultaneously, a cross-view contrastive learning module is introduced in SCMVC to assimilate the consensus information across different views. In addition, to distinguish the differences in semantic information across different views, we incorporate an attention mechanism to determine the relative importance of each view in the semantic information fusion. The proposed model is jointly optimized by training and clustering to mutually reinforce each other, gradually improving its performance and robustness. Experiments on three real-world multi-view datasets demonstrate the effectiveness of our SCMVC framework. Source codes are freely available at https://github.com/HexonBang/SCMVC.

Detecting differentially expressed genes from RNA-seq data using fuzzy clustering.

A two-group comparison test is generally performed on RNA sequencing data to detect differentially expressed genes (DEGs). However, the accuracy of this method is low due to the small sample size. To address this, we propose a method using fuzzy clustering that artificially generates data with expression patterns similar to those of DEGs to identify genes that are highly likely to be classified into the same cluster as the initial cluster data. The proposed method is advantageous in that it does not perform any test. Furthermore, a certain level of accuracy can be maintained even when the sample size is biased, and we show that such a situation may improve the accuracy of the proposed method. We compared the proposed method with the conventional method using simulations. In the simulations, we changed the sample size and difference between the expression levels of group 1 and group 2 in the DEGs to obtain the desired accuracy of the proposed method. The results show that the proposed method is superior in all cases under the conditions simulated. We also show that the effect of the difference between group 1 and group 2 on the accuracy is more prominent when the sample size is biased.

Enhanced classification of pyrite generations based on mineral chemistry using uniform manifold approximation and projection (UMAP)

Trace element signatures are tracked to unravel the genetic history of ore deposits in several mineral systems. This is particularly true for pyrite, a ubiquitous component of many ore-forming systems, including orogenic gold deposits. Here, we critically compare the efficacy of utilizing Uniform Manifold Approximation and Projection (UMAP) versus Principal Component Analysis (PCA) as dimensionality reduction tools applied to a 31 element dataset collected using Laser Ablation Inductively Coupled Mass Spectrometry of pyrite grains from the Kibali gold district (Democratic Republic of Congo). Because of the non-linearity inherent to mineral chemistry and because of its superior preservation of local (distances within clusters) and global (separation between clusters) data relationships, the UMAP approach outperforms dimensionality reduction by PCA. We further present a workflow in which UMAP dimensionality reduction is followed by k-means clustering to guide the classification of pyrite generations in the Kibali case study. Validating this approach with trace elements and UMAP + k-means on a seed-by-seed petrography basis shows that the workflow significantly enhances the original pyrite classification, previously based on texture. This study thus emphasizes the utility of employing advanced statistical analysis methods to capture the intricate nature of pyrite formation. These findings will shape best practices for handling large multi-element datasets in pyrite mineral chemistry studies and are extrapolatable to other mineral systems in which trace element signatures are used to infer the conditions of ore deposit genesis.

Multimode high‐dimensional time series clustering and monitoring for wind turbine SCADA data

AbstractThe operating process of complex systems usually manifest in multiple distinct operating modes. In the case of a wind turbine, for example, its operating mode is highly influenced by the wind condition, which changes dynamically in natural environment. The SCADA system plays a crucial role in collecting various parameters from wind turbines, facilitating the differentiation, and modeling of distinct operating modes. However, the challenge lies in the excessive dimensionality of variables in SCADA data, making modeling efforts both intricate and inefficient. In this study, we leverage the engineering knowledge on the hierarchical structure of the variables in wind turbine, and propose a novel method to efficiently cluster the data temporally by operating modes. Our methodology involves initially clustering variables according to subsystems and implementing temporal clustering within each subsystem. Subsequently, we introduce a novel graph neural network to extract and concatenate features from all subsystems, enabling the discrimination of the operational mode of the entire system. Finally, we model these features to make predictions of the output power, and the prediction residual can be used for monitoring. Performance evaluations on both numerical experiments and real‐world wind turbine datasets attest to the effectiveness and superiority of the proposed methods.

A momentum quantification method for tennis matches based on t-SNE and k means clustering

Momentum is a crucial factor influencing the outcomes of sports competitions. This study proposes a method for visualising and quantifying momentum using various modern analytical techniques. These techniques include feature engineering, dimensionality reduction with the t-SNE algorithm, and k-means clustering. By analysing data from the past five years of Wimbledon, the study extracted various performance features such as serve success rate and scoring rate. By calculating the similarities between features and conducting cluster analysis, the study reveals changes in momentum during matches. This research employs data visualisation and clustering results to not only display the conditions of matches under different momentum states but also quantifies momentum changes by calculating the Euclidean distance between data points and cluster centroids and standardising these distances using the Z-score method. This approach provides a visual perspective that reveals the dynamic changes that occur within matches and quantifies momentum. It demonstrates that these changes in momentum can indeed reflect the progression and outcomes of matches.

Do Cognitive Subtypes Exist in People at Clinical High Risk for Psychosis? Results From the EU-GEI Study.

Cognition has been associated with socio-occupational functioning in individuals at Clinical High Risk for Psychosis (CHR-P). The present study hypothesized that clustering CHR-P participants based on cognitive data could reveal clinically meaningful subtypes. A cohort of 291 CHR-P subjects was recruited through the multicentre EU-GEI high-risk study. We explored whether an underlying cluster structure was present in the cognition data. Clustering of cognition data was performed using k-means clustering and density-based spatial clustering of applications with noise. Cognitive subtypes were validated by comparing differences in functioning, psychosis symptoms, transition outcome, and grey matter volume between clusters. Network analysis was used to further examine relationships between cognition scores and clinical symptoms. No underlying cluster structure was found in the cognitive data. K-means clustering produced "spared" and "impaired" cognition clusters similar to those reported in previous studies. However, these clusters were not associated with differences in functioning, symptomatology, outcome, or grey matter volume. Network analysis identified cognition and symptoms/functioning measures that formed separate subnetworks of associations. Stratifying patients according to cognitive performance has the potential to inform clinical care. However, we did not find evidence of cognitive clusters in this CHR-P sample. We suggest that care needs to be taken in inferring the existence of distinct cognitive subtypes from unsupervised learning studies. Future research in CHR-P samples could explore the existence of cognitive subtypes across a wider range of cognitive domains.

Diverse representation-guided graph learning for multi-view metric clustering

Multi-view graph clustering has garnered tremendous interest for its capability to effectively segregate data by harnessing information from multiple graphs representing distinct views. Despite the advances, conventional methods commonly construct similarity graphs straightway from raw features, leading to suboptimal outcomes due to noise or outliers. To address this, latent representation-based graph clustering has emerged. However, it often hypothesizes that multiple views share a fixed-dimensional coefficient matrix, potentially resulting in useful information loss and limited representation capabilities. Additionally, many methods exploit Euclidean distance as a similarity metric, which may inaccurately measure linear relationships between samples. To tackle these challenges, we develop a novel diverse representation-guided graph learning for multi-view metric clustering (DRGMMC). Concretely, raw sample matrix from each view is first projected into diverse latent spaces to capture comprehensive knowledge. Subsequently, a popular metric is leveraged to adaptively learn similarity graphs with linearity-aware based on attained coefficient matrices. Furthermore, a self-weighted fusion strategy and Laplacian rank constraint are introduced to output clustering results directly. Consequently, our model merges diverse representation learning, metric learning, consensus graph learning, and data clustering into a joint model, reinforcing each other for holistic optimization. Substantial experimental findings substantiate that DRGMMC outperforms most advanced graph clustering techniques.

Fast Characterization of Optically Detected Magnetic Resonance Spectra via Data Clustering

Fast Characterization of Optically Detected Magnetic Resonance Spectra via Data Clustering

Integrated sources model: A new space-learning model for heterogeneous multi-view data reduction, visualization, and clustering

In machine learning, multi-view data involve multiple distinct sets of attributes (“views”) for a common set of observations; when each view has the same attributes considered in different contexts, the data are said to contain multiple views of homogeneous format, which can be conceptualized as a tensor. In this article, we describe a novel approach for integrating multiple views of heterogeneous format into a common latent space using a workflow that involves non-negative matrix and tensor factorization (NMF/NTF). This approach, which we refer to as the integrated sources model (ISM), consists of two main steps: Embedding and analysis. In the embedding step, the views are transformed into matrices with common non-negative components. In the analysis step, the transformed views are combined into a tensor and decomposed using NTF. We also present a variant of ISM; the integrated latent sources model (ILSM), which offers significant advantages over ISM in terms of computational power and in cases where the views are highly unbalanced with regard to the number of attributes per view. Noteworthy, ISM can be extended to process multi-omic and multi-view datasets even in the presence of missing views. We provide a proof-of-concept analysis using five examples, including the UCI Digits (the University of California Irvine Pen-Based Recognition of Handwritten Digits) dataset, a public cell-type gene signatures dataset, and a multi-omic single-cell dataset. These examples demonstrate that, in most cases, multi-view clustering is better achieved with ISM or its variant ILSM than with other latent space approaches. We also show how the non-negativity and sparsity of the ISM model components enable straightforward interpretations, in contrast to other approaches that involve latent factors of mixed signs. Finally, we present potential applications to single-cell multi-omics and spatial mapping, including spatial imaging, spatial transcriptomics, and computational biology, which are currently under evaluation. ISM relies on state-of-the-art algorithms invoked through a simple workflow implemented in Python.

A Novel Hierarchical High-Dimensional Unsupervised Active Learning Method

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 K2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${K}^{2}$$\\end{document}-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.

Overcoming mechanistic limitations of process-based phenological models: A data clustering method for large-scale applications

Process-based phenological models use thermal requirement (TR) defined by planting date, temperature and photoperiod to predict crop developmental stages. The TR value for a specific developmental stage for a given variety is often presumed to be constant regardless of environmental conditions. We calibrated and compared 12 phenological models using 27-year of observation data of one unique rice (Oryza sativa L.) variety ('Shanyou63') from 46 sites in southern China. Our findings indicated that shifts in environmental conditions significantly affected TR values, e.g., standard deviations of TR at physiological maturation ranged from 83 to 167 °C d. Clustering sites together minimized environmental heterogeneity, and thus minimized the differences in TR for different phenological stages. When the increased from 1 to 24, simulation errors for the 12 models showed a significant decrease across all developmental stages, from 1.8 to 1.4 days for tillering, from 5.3 to 3.7 days for jointing, from 5.6 to 3.9 days for booting, from 4.7 to 3.3 days for heading, and from 6.6 to 3.7 days for physiological maturation. Furthermore, our findings indicate that models featuring a three-segment piecewise linear temperature response function provide a more precise prediction. In contrast, models incorporating a Beta temperature response function have not performed well. This difference is attributed to different mechanisms used to describe the response of crop development rate to temperature, particularly at non-optimum temperatures. The impact of the photoperiod response function on prediction accuracy became significant with the expansion of scale. Our results demonstrate that clustering method effectively compensates for the lack of crop adaptation processes in common phenological models, leading to significantly improved phenological prediction accuracy in regions with environmental diversity.

Effect of Soil Tillage Systems and Crop Seasonality on the Sediment Geochemical Properties Transferred into a Headwater: A Case of Study in Tobacco Plantation

ABSTRACT The global impacts of agricultural land conversion on soil erosion and pollution, particularly in tobacco cultivation areas, are well-recognized as significant contributors to soil degradation. These areas are identified as hotspots for environmental concerns due to practices that lead to increased erosion and pollution. From this perspective, this case of study explores fine sediment samples from two areas with tobacco cultivation under different tillage systems and seasonal variations, transport into a headwater, and evaluates, on a local scale: (1) the impact of tillage systems on the geochemical signature of sediments; (2) if whether crop seasonality affects these sediment geochemical signatures. The Conventional Ridge Tillage (CRT) system involves extensive soil exposure and machinery for soil management, while the Mulch Ridge Tillage (MRT) system prioritizes soil conservation and relies on herbicides for weed control. The analytical methodology used to assess the sample element characteristics was Energy Dispersive X-ray Fluorescence (EDXRF). It was applied on the twenty fine sediments (ten of harvest and ten of inter-harvest season of tobacco) to quantitatively assess their inorganic composition. Additionally, Pearson correlation analysis, Hierarchical Cluster Analysis (HCA), and Principal Component Analysis (PCA) were applied on the EDXRF data to highlight the similarities and, thus, providing information to assess the complex data clustering patterns. As a result, the sediment compositions from the two studied soil systems are not similar. The PCA showed that the CRT sediments are characterized by the P, S, K, Ca, and Mn content, presenting a geochemical signature related to manure and fertilizer compared to the MRT, which is correlated with Al, Ti, Fe, Cu, and Zn contents, exhibiting a geochemical signature characterized by the natural soil composition. Therefore, the sediment geochemical signatures might be affected by two phases in the study area: a) tillage system characteristics and b) seasonal soil erosion. These findings underscore the importance of managing soil nutrients to mitigate soil pollution and nutrient exportation to aquatic systems. Moreover, the results emphasize the recommendations for sustainable agricultural practices in tobacco-growing areas to protect environmental quality.

Vanadium oxide clusters in substellar atmospheres. A quantum chemical study

As a refractory material, vanadia (solid V$_2$O$_5$) is likely to be found as a condensate in the atmospheres of substellar objects such as exoplanets and brown dwarfs. However, the nature of the nanometer-sized vanadium oxide clusters that partake in the nucleation process is not well understood. We aim to understand the formation of cloud condensation nuclei in oxygen-rich substellar atmospheres by calculating the relevant fundamental properties of the energetically most favorable vanadium oxide molecules and clusters and, investigate how they contribute to the formation of condensation seeds. We applied a hierarchical optimization approach in order to find the most favourable structures for clusters of (VO) N and (VO$_2$) N for N=1-10, and of (V$_2$O$_5$) N for N=1-4, and to calculate their thermodynamical potentials. The candidate geometries are initially optimized by applying classical interatomic potentials; these are then refined at the B3LYP/cc-pVTZ level of theory to obtain accurate zero-point energies and thermochemical quantities. We present previously unreported vanadium oxide cluster structures as the lowest-energy isomers. Moreover, we report revised cluster energies and their thermochemical properties. Chemical equilibrium calculations are used to asses the impact of the updated and newly derived thermodynamic potentials on the gas-phase abundances of vanadium-bearing species. In chemical equilibrium, larger clusters from different stoichiometric families are found to be the most abundant vanadium-bearing species for temperatures below sim 1000 K, while molecular VO is the most abundant between sim 1000 K and sim 2000 K. We determine the nucleation rates of each stoichiometric family for a given (T$_ gas $, p$_ gas $) profile of a brown dwarf using both classical and non-classical nucleation theory. Small differences in the revised Gibbs free energies of the clusters have a large impact on the abundances of vanadium-bearing species in chemical equilibrium at temperatures below sim 1000 K. These abundance changes subsequently have an impact on the nucleation rates of each stoichiometric family. We find that with the revised and more accurate cluster data, non-classical nucleation rates are up to 15 orders of magnitude higher than classical nucleation rates.

An Effective Crow Search Algorithm and Its Application in Data Clustering

An Effective Crow Search Algorithm and Its Application in Data Clustering

Metabolic model guided CRISPRi identifies a central role for phosphoglycerate mutase in Chlamydia trachomatis persistence.

This study uses a metabolic model to investigate factors that contribute to the persistence of Chlamydia trachomatis serovar L2 (CTL) under tryptophan and iron starvation conditions. As CTL lacks many canonical transcriptional regulators, the model was used to assess two prevailing hypotheses on persistence-that the chlamydial response to nutrient starvation represents a passive response due to the lack of regulators or that it is an active response by the bacterium. K-means clustering of stress-induced transcriptomics data revealed striking evidence in favor of the lack of adaptive (i.e., a passive) response. To find the metabolic signature of this, metabolic modeling pin-pointed pgm as a potential regulator of persistence. Thermodynamic driving force, enzyme cost, and CRISPRi knockdown of pgm supported this finding. Overall, this work introduces thermodynamic driving force and enzyme cost as a tool to understand chlamydial persistence, demonstrating how systems biology-guided CRISPRi can unravel complex bacterial phenomena.

Phase identification and micromechanical properties of non-traditional and natural pozzolan based alkali-activated materials

Four groups of non-traditional and natural pozzolan (NNP) based (aluminosilicate-based) materials, calcined clays, ground bottom ashes, volcanic ashes, and fluidized bed combustion ashes, were alkali activated using a hybrid solution of sodium silicate and sodium hydroxide. The microhardness and reduced modulus of the phases present in eleven alkali-activated pastes were measured using nanoindentation. At least 800 data points were collected from 28-day old ambiently cured paste samples. Unsupervised machine learning techniques i.e., k-means clustering and Gaussian mixture modeling (GMM) were used for the classification of the micromechanical properties supplemented by scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS). The clustering of micromechanical and EDS data revealed five distinct phases in these hardened pastes. The alkali-activated gels identified had reduced modulus values ranging from 12 to 34 GPa. The remnant phases were partly reacted grains and unreacted minerals exhibiting reduced modulus values ranging from 35 to 105 GPa. Micromechanical property contour plots and SEM image pixel count plots were used to validate the phase assemblage. Additionally, the identification capabilities and advantages of the method are assessed by direct comparison with experimental results for different alkali-activated gels.

Use of distinct projective techniques in investigating participants' perception of a clean label dairy product: A study on the presence of additives/stabilizers in Ultra high temperature processing milk's label in Brazil

AbstractUsing an online questionnaire, the perception of additives/stabilizers in ultra‐high temperature (UHT) milk was investigated using different projective techniques. Clustering data on the Food Choice Questionnaire (FCQ) identified two groups of participants, Moderates and Judicious, with the latter assigning significantly higher agreement scores for all FCQ items. In the Word Association task, clean label UHT milk was more associated with “Nutrients and Constituents” by the Moderates and less by the Judicious, suggesting that for part of the interviewed participants, the absence of stabilizers increases the general quality of the product. In the Product Personality Profiling task, the same product was related to “young people” and a “balanced diet” by the Moderates. At the same time, the Judicious was designed for a stereotype of healthy living. Finally, the methodologies used provide valuable insights into the dairy sector, holistically highlighting the nuances in consumer preferences and expectations, offering a significant strategic opportunity for developing and promoting new clean‐label products in the segment.Practical ApplicationInvestigating consumer perceptions regarding the absence of additives offers valuable insights for marketing strategies and product development aimed at different consumer market segments, especially the dairy sector, and contributes to consumer psychology and behavioral research. At the same time, the food industry can use these findings to improve attribute communication, build consumer confidence, and adapt products to identified preferences, reflecting a practical intersection between academia and the industry.