Clustering Solutions Research Articles

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.

Identifying and Prioritizing Factors to Improve Implementation of an Evidence-Based Program for Child Maltreatment Prevention in Rural Missouri Communities

Prevention of child abuse and maltreatment is critical in the Ozark region of Missouri where rates are higher than in other areas of the state. Community-based organizations seeking to address these issues in rural areas face significant challenges both internal and external to the organization. Guided by the Consolidated Framework for Implementation Research (CFIR), Whole Kids Outreach (WKO) utilized a modified concept mapping approach to identify strategies needed to address these challenges and more effectively implement the Healthy Families America (HFA) program in seven counties in the rural Missouri Ozark region. Semi-structured interviews were used to identify barriers and supports to the implementation of HFA. Statements were generated via in-person and telephone interviews with key stakeholders (WKO internal staff and external partners). These stakeholders then sorted statements into like piles and rated them according to importance. Concept mapping systems software, using multidimensional scaling and hierarchical cluster analysis, was used to determine the most salient cluster solution. The resulting cluster map depicted six thematic clusters and provided a visual representation of relationships between clusters. Stakeholders were then guided through a nominal group process to prioritize key issues and begin to develop strategies to address them.

Cluster dynamics study on nano damage of RPV steels under proton irradiation at 290°C

Irradiation-induced defects such as dislocation loops, cavities or solute clusters and chemical composition segregation of reactor pressure vessel (RPV) steel are the root causes of irradiation embrittlement. Combining two nucleation mechanisms, namely, the uniform nucleation and non-uniform nucleation of solute clusters (such as Cu-rich phase), a cluster kinetic simulation was established based on the reaction rate theory, and the co-evolution of matrix damage and Cu-rich phase in low-copper RPV steel was simulated under irradiation. And the average size and number density of defective clusters and solute clusters were established with irradiation dose. Compared with the average size and number density of dislocation loops observed by transmission electron microscopy (TEM) of proton irradiated RPV steel at 290°C, the verification results show that the cluster dynamics model considering both the nucleation mechanism of interstitial dislocation loops and vacancy clusters can well simulate the irradiation damage behavior of materials.

The connectivity-based architecture of the human piriform cortex

The anatomy of the human piriform cortex (PC) is poorly understood. We used a bimodal connectivity-based-parcellation approach to investigate subregions of the PC and its connectional differentiation from the amygdala.One hundred (55 % female) genetically unrelated subjects from the Human Connectome Project were included. A region of interest (ROI) was delineated bilaterally covering PC and amygdala, and functional and structural connectivity of this ROI with the whole gray matter was computed. Spectral clustering was performed to obtain bilateral parcellations at granularities of k = 2–10 clusters and combined bimodal parcellations were computed. Validity of parcellations was assessed via their mean individual-to-group similarity per adjusted rand index (ARI).Individual-to-group similarity was higher than chance in both modalities and in all clustering solutions. The amygdala was clearly distinguished from PC in structural parcellations, and olfactory amygdala was connectionally more similar to amygdala than to PC. At higher granularities, an anterior and ventrotemporal and a posterior frontal cluster emerged within PC, as well as an additional temporal cluster at their boundary. Functional parcellations also showed a frontal piriform cluster, and similar temporal clusters were observed with less consistency. Results from bimodal parcellations were similar to the structural parcellations. Consistent results were obtained in a validation cohort.Distinction of the human PC from the amygdala, including its olfactory subregions, is possible based on its structural connectivity alone. The canonical fronto-temporal boundary within PC was reproduced in both modalities and with consistency. All obtained parcellations are freely available.

Model-based multifacet clustering with high-dimensional omics applications.

High-dimensional omics data often contain intricate and multifaceted information, resulting in the coexistence of multiple plausible sample partitions based on different subsets of selected features. Conventional clustering methods typically yield only one clustering solution, limiting their capacity to fully capture all facets of cluster structures in high-dimensional data. To address this challenge, we propose a model-based multifacet clustering (MFClust) method based on a mixture of Gaussian mixture models, where the former mixture achieves facet assignment for gene features and the latter mixture determines cluster assignment of samples. We demonstrate superior facet and cluster assignment accuracy of MFClust through simulation studies. The proposed method is applied to three transcriptomic applications from postmortem brain and lung disease studies. The result captures multifacet clustering structures associated with critical clinical variables and provides intriguing biological insights for further hypothesis generation and discovery.

A Comparative Analysis of Machine Learning Algorithms for Identifying Cultural and Technological Groups in Archaeological Datasets through Clustering Analysis of Homogeneous Data

Machine learning algorithms have revolutionized data analysis by uncovering hidden patterns and structures. Clustering algorithms play a crucial role in organizing data into coherent groups. We focused on K-Means, hierarchical, and Self-Organizing Map (SOM) clustering algorithms for analyzing homogeneous datasets based on archaeological finds from the middle phase of Pre-Pottery B Neolithic in Southern Levant (10,500–9500 cal B.P.). We aimed to assess the repeatability of these algorithms in identifying patterns using quantitative and qualitative evaluation criteria. Thorough experimentation and statistical analysis revealed the pros and cons of each algorithm, enabling us to determine their appropriateness for various clustering scenarios and data types. Preliminary results showed that traditional K-Means may not capture datasets’ intricate relationships and uncertainties. The hierarchical technique provided a more probabilistic approach, and SOM excelled at maintaining high-dimensional data structures. Our research provides valuable insights into balancing repeatability and interpretability for algorithm selection and allows professionals to identify ideal clustering solutions.

Analytical Solutions of Symmetric Isotropic Spin Clusters Using Spin and Point Group Projectors

Spin models like the Heisenberg Hamiltonian effectively describe the interactions of open-shell transition-metal ions on a lattice and can account for various properties of magnetic solids and molecules. Numerical methods are usually required to find exact or approximate eigenstates, but for small clusters with spatial symmetry, analytical solutions exist, and a few Heisenberg systems have been solved in closed form. This paper presents a simple, generally applicable approach to analytically solve isotropic spin clusters, based on adapting the basis to both total spin and point group symmetry to factor the Hamiltonian matrix into sufficiently small blocks. We demonstrate applications to small rings and polyhedra, some of which are straightforward to solve by successive spin-coupling for Heisenberg terms only; additional interactions, such as biquadratic exchange or multi-center terms necessitate symmetry adaptation.

Novel Insights into the Catalytic Mechanism of Collagenolysis by Zn(II)-Dependent Matrix Metalloproteinase-1.

Collagen hydrolysis, catalyzed by Zn(II)-dependent matrix metalloproteinases (MMPs), is a critical physiological process. Despite previous computational investigations into the catalytic mechanisms of MMP-mediated collagenolysis, a significant knowledge gap in understanding remains regarding the influence of conformational sampling and entropic contributions at physiological temperature on enzymatic collagenolysis. In our comprehensive multilevel computational study, employing quantum mechanics/molecular mechanics (QM/MM) metadynamics (MetD) simulations, we aimed to bridge this gap and provide valuable insights into the catalytic mechanism of MMP-1. Specifically, we compared the full enzyme-substrate complex in solution, clusters in solution, and gas-phase to elucidate insights into MMP-1-catalyzed collagenolysis. Our findings reveal significant differences in the catalytic mechanism when considering thermal effects and the dynamic evolution of the system, contrasting with conventional static potential energy surface QM/MM reaction path studies. Notably, we observed a significant stabilization of the critical tetrahedral intermediate, attributed to contributions from conformational flexibility and entropy. Moreover, we found that protonation of the scissile bond nitrogen occurs via proton transfer from a Zn(II)-coordinated hydroxide rather than from a solvent water molecule. Following C-N bond cleavage, the C-terminus remains coordinated to the catalytic Zn(II), while the N-terminus forms a hydrogen bond with a solvent water molecule. Subsequently, the release of the C-terminus is facilitated by the coordination of a water molecule. Our study underscores the pivotal role of protein conformational dynamics at physiological temperature in stabilizing the transition state of the rate-limiting step and key intermediates, compared to the corresponding reaction in solution. These fundamental insights into the mechanism of collagen degradation provide valuable guidance for the development of MMP-1-specific inhibitors.

Possibility and stabilizing effect of Mo clusters in the Ni-based single-crystal superalloy

Nickel-based single-crystal superalloys are crucial materials for the preparation of aero-engine turbine blades. Many solute elements are added to superalloys for strengthening. However, the relationship between the clustering behavior of solute atoms and the properties of nickel-based single-crystal superalloys is still unclear. Herein, we conduct first-principles calculations on γ phases with Mo−Mo and Mo−Mo−Ru clusters to reveal the possibility and stabilizing mechanism of solute clusters. Introducing Mo lowers the total energy, binding energy, and formation energy of the γ phase due to the replacement of weak Ni−Ni interaction with strong Mo−Ni bonding. Note that the γ phase containing the Mo−Mo cluster is more stable than that containing a Mo single atom, possibly owing to a wide affecting range. The Ru atom added to the γ phase can further boost system stability, and it tends to form a Mo−Mo−Ru cluster. The stabilizing impact of the Mo−Mo−Ru cluster is demonstrated to be the replacement of weak Ni−Mo interaction by the strong Ru−Mo interaction, which may be derived from the enhanced d-orbital hybridization.

On the diffusive model of C<sub>60</sub> fullerene clusterization in liquids

Purpose of research. The purpose of the study is to explain the mechanisms of clustering of fullerene molecules in liquid media observed using various structural nuclear -physical methods, as well as the interpretation of experimental data within the framework of a microscopic diffusion model. Methods. The article gives a brief overview of the results of positron annihilation spectroscopy, small-angle neutron scattering, translucent electron microscopy, with which the geometric parameters of fullerene clusters in solutions were established. The theoretical method of research is the microscopic diffusion model “Limited diffusion aggregation”, which describes the kinetic processes of clusterization. Results. The diffusion limited aggregation model displays adequately the mechanism of formation of C60 molecules in the form of fractal units in the volume of fluid, observed in experiments on positron annihilation spectroscopy and small-angle neutron scattering. The structural indicators of aggregate fullerene particles in carbon disulfide, o-Xylene, toluene, water and other solvents are analyzed. The properties of various diffusion models (the reaction limited aggregation model and the diffusion limited aggregation model) are considered and their combination in relation to the assessmentsof the kinetics of fullerene aggregation in solutions. A quantitative comparison of the results of the discussed models was carried out using the example of carbon disulfide. Conclusion. The diffusion microscopic models adequately describe the phenomenon of fullerene aggregation in polar and non-polar solvents, which are recorded by various nuclear methods (positron annihilating spectroscopy and small-angle neutron scattering), the most reliable is the diffusion limited aggregation model, more than that, it is the basis of a numerical definition of structural structures parameters of units. Compared to neutron scattering, with the annihilation of positrons, in the aggregation of fullerene the [Ps–C60] molecular complexes participate in the clusterization, but this does not affect the change in the size of the cluster and the reliability of the results.

Shear clustering in solution: Polyelectrolytes compared to uncharged polymers

Shear clustering in solution: Polyelectrolytes compared to uncharged polymers

A New Approach to Individualize Physical Activity Interventions for Individuals With Dementia: Cluster Analysis Based on Physical and Cognitive Performance.

Physical activity (PA) can have a beneficial effect on cognitive and physical performance in individuals with dementia (IWD), including those residing in nursing homes. However, PA interventions in nursing homes are usually delivered using a group setting, which may limit the effectiveness of the intervention due to the heterogenous nature of IWD. Therefore, the purpose of this study was to identify clusters based on cognitive and physical performance values, which could be used to improve individualization of PA interventions. Based on the cognitive and physical performance variables of 230 IWD, a cluster analysis was conducted. Global cognition (Mini-Mental State Examination), mobility (6-Meter Walking Test), balance (Frailty and Injuries: Cooperative Studies of Intervention Techniques-subtest-4), and strength and function of lower extremities (30-Second Chair-Stand Test) were assessed, and values were used to perform a hierarchical cluster analysis with Ward's method. Differences in physical and cognitive performance as well as other secondary outcomes (age, sex, body mass index, use of walking aids, diagnosis and etiology of dementia, number of medications, and Cumulative Illness Rating Scale) were tested using 1-factorial analyses of variance. Out of 230 data sets, 3-cluster solutions were identified with similar cluster sizes of 73 to 79. The silhouette coefficients for all calculated clusters ranged between 0.15 and 0.34. The cluster solutions were discussed in the context of cognitive and physical functions as well as training modalities and opportunities. The 4-cluster solution appears to be best suited for providing or developing an individualized PA intervention. The identified clusters of the 4-cluster solution may be used in future research to improve individualization of dementia-specific PA interventions. By assigning IWD to these clusters, more homogenous groups with regard to cognitive and physical performance can be formed. This allows for more individualized PA interventions and may result in a higher effectiveness, particularly in nursing homes. Our findings are relevant for therapists and nursing staff who design or deliver PA interventions in nursing homes or similar settings.

Sketch-and-solve approaches to k-means clustering by semidefinite programming

Abstract We study a sketch-and-solve approach to speed up the Peng–Wei semidefinite relaxation of $k$-means clustering. When the data are appropriately separated we identify the $k$-means optimal clustering. Otherwise, our approach provides a high-confidence lower bound on the optimal $k$-means value. This lower bound is data-driven; it does not make any assumption on the data nor how they are generated. We provide code and an extensive set of numerical experiments where we use this approach to certify approximate optimality of clustering solutions obtained by k-means++.

Mendorong Pertumbuhan Pangsa Pasar B2B untuk Sektor Ride-Hailing Menggunakan Segmentasi Pasar Strategis

The expansion of Indonesia's ride-hailing sector has been considerable, fueled by technological advancements and the widespread embrace of smartphones. Despite its swift growth, the industry faces difficulties concerning long-term viability, safety issues, and compliance with governmental regulations. Nevertheless, the integration of advanced technologies and strategic plans for service expansion into new regions presents significant opportunities. The competitive environment in Indonesia's ride-hailing market not only stimulates innovation but also shapes a lively and evolving market atmosphere. Originally designed for consumers, the ride-hailing sector has evolved into a versatile transportation solution for various business needs, including employee transportation and goods delivery. These services offer advantages for companies, such as enhanced operational efficiency and reduced logistics costs. Recognizing the diversity of the business market, segmentation becomes vital in comprehending customer needs. Through tailored marketing approaches, companies can deliver more pertinent solutions, boosting competitiveness and enlarging B2B market share. By using K-means clustering, it yields 5 clusters, namely cluster 1: Tech Innovators and Financial Players, cluster 2: Logistics Singular Focus, cluster 3: Logistics, Retail, and Automotive Synergy, cluster 4: Culinary, Logistics, and Travel Dynamics, cluster 5: Tech Titans, Healthcare Giants, and Financial Leaders. The analysis of user clusters on the B2B Ride Hailing Indonesia platform provides useful insights that guide strategic recommendations for improving service offerings, refining marketing strategies, and optimizing business operations. Targeting the millennial demographic through digital channels and influencers, examining marginal costs for high-traffic clusters to identify optimization opportunities, exploring expansion possibilities in clusters with growth potential, and tailoring business solutions for clusters with unique needs are among the recommendations. Keywords: K-means Clustering, Market Innovation, Market Share Expansion, Ride-Hailing Sector, Segmentation, Strategic Decisions.

Compression-based Aggregation and Clustering Techniques for XML Enhanced Performance

This paper has presented a comprehensive literature study and discusses the primary problem of network congestion and bottlenecks in XML applications. The explosive growth of Web services has caused a significant problem in network traffic due to the largely repetitive XML textual structures. The challenge of XML message size reduction in web services has been examined, focusing on new approaches such as compression methods and message clustering for Aggregation and compression of XML messages efficiently. The goal is to examine how different strategies affect network traffic. This overview discusses twenty-five publications categorized into four: compression techniques, aggregation approaches, clustering for web services aggregation approaches, and other web services clustering models. In comparison to all models, evaluation has shown that web service clustering solutions efficiently reduce traffic. Technically, the web service clustering for aggregation models has achieved an excellent average compression ratio of up to (14.12).

Ensemble clustering via synchronized relabelling

Ensemble clustering is an important problem in unsupervised learning that aims at aggregating multiple noisy partitions into a unique clustering solution. It can be formulated in terms of relabelling and voting, where relabelling refers to the task of finding optimal permutations that bring coherence among labels in input partitions. In this paper we propose a novel solution to the relabelling problem based on permutation synchronization. By effectively circumventing the need for a reference clustering, our method achieves superior performance than previous work under varying assumptions and scenarios, demonstrating its capability to handle diverse and complex datasets.

Establishing breather and N-soliton solutions for conformable Klein–Gordon equation

Abstract This article develops and investigates the behavior of soliton solutions for the spatiotemporal conformable Klein–Gordon equation (CKGE), a well-known mathematical physics model that accounts for spinless pion and de-Broglie waves. To accomplish this task, we deploy an effective analytical method, namely, the modified extended direct algebraic method (mEDAM). This method first develops a nonlinear ordinary differential equation (NODE) through the use of a wave transformation. With the help of generalized Riccati NODE and balancing nonlinearity with the highest derivative term, it then assumes a finite series-form solution for the resulting NODE, from which four clusters of soliton solutions – generalized rational, trigonometric, exponential, and hyperbolic functions – are derived. Using contour and three-dimensional visuals, the behaviors of the soliton solutions – which are prominently described as dark kink, bright kink, breather, and other N N -soliton waves – are examined and analyzed. These results have applications in solid-state physics, nonlinear optics, quantum field theory, and a more thorough knowledge of the dynamics of the CKGE.

FFT-Based Probability Density Imaging of Euler Solutions.

When using traditional Euler deconvolution optimization strategies, it is difficult to distinguish between anomalies and their corresponding Euler tails (those solutions are often distributed outside the anomaly source, forming "tail"-shaped spurious solutions, i.e., misplaced Euler solutions, which must be removed or marked) with only the structural index. The nonparametric estimation method based on the normalized B-spline probability density (BSS) is used to separate the Euler solution clusters and mark different anomaly sources according to the similarity and density characteristics of the Euler solutions. For display purposes, the BSS needs to map the samples onto the estimation grid at the points where density will be estimated in order to obtain the probability density distribution. However, if the size of the samples or the estimation grid is too large, this process can lead to high levels of memory consumption and excessive computation times. To address this issue, a fast linear binning approximation algorithm is introduced in the BSS to speed up the computation process and save time. Subsequently, the sample data are quickly projected onto the estimation grid to facilitate the discrete convolution between the grid and the density function using a fast Fourier transform. A method involving multivariate B-spline probability density estimation based on the FFT (BSSFFT), in conjunction with fast linear binning appropriation, is proposed in this paper. The results of two random normal distributions show the correctness of the BSS and BSSFFT algorithms, which is verified via a comparison with the true probability density function (pdf) and Gaussian kernel smoothing estimation algorithms. Then, the Euler solutions of the two synthetic models are analyzed using the BSS and BSSFFT algorithms. The results are consistent with their theoretical values, which verify their correctness regarding Euler solutions. Finally, the BSSFFT is applied to Bishop 5X data, and the numerical results show that the comprehensive analysis of the 3D probability density distributions using the BSSFFT algorithm, derived from the Euler solution subset of x0,y0,z0, can effectively separate and locate adjacent anomaly sources, demonstrating strong adaptability.

Bridging the sim2real gap. Investigating deviations between experimental motion measurements and musculoskeletal simulation results-a systematic review.

Musculoskeletal simulations can be used to estimate biomechanical variables like muscle forces and joint torques from non-invasive experimental data using inverse and forward methods. Inverse kinematics followed by inverse dynamics (ID) uses body motion and external force measurements to compute joint movements and the corresponding joint loads, respectively. ID leads to residual forces and torques (residuals) that are not physically realistic, because of measurement noise and modeling assumptions. Forward dynamic simulations (FD) are found by tracking experimental data. They do not generate residuals but will move away from experimental data to achieve this. Therefore, there is a gap between reality (the experimental measurements) and simulations in both approaches, the sim2real gap. To answer (patho-) physiological research questions, simulation results have to be accurate and reliable; the sim2real gap needs to be handled. Therefore, we reviewed methods to handle the sim2real gap in such musculoskeletal simulations. The review identifies, classifies and analyses existing methods that bridge the sim2real gap, including their strengths and limitations. Using a systematic approach, we conducted an electronic search in the databases Scopus, PubMed and Web of Science. We selected and included 85 relevant papers that were sorted into eight different solution clusters based on three aspects: how the sim2real gap is handled, the mathematical method used, and the parameters/variables of the simulations which were adjusted. Each cluster has a distinctive way of handling the sim2real gap with accompanying strengths and limitations. Ultimately, the method choice largely depends on various factors: available model, input parameters/variables, investigated movement and of course the underlying research aim. Researchers should be aware that the sim2real gap remains for both ID and FD approaches. However, we conclude that multimodal approaches tracking kinematic and dynamic measurements may be one possible solution to handle the sim2real gap as methods tracking multimodal measurements (some combination of sensor position/orientation or EMG measurements), consistently lead to better tracking performances. Initial analyses show that motion analysis performance can be enhanced by using multimodal measurements as different sensor technologies can compensate each other's weaknesses.

K-Mean Based Hyper-Metaheuristic Grey Wolf and Cuckoo Search Optimizers for Automatic MRI Medical Image Clustering

In this paper a new clustering algorithm is proposed for optimal clustering of MRI medical image. In our proposed algorithm, the clustering process implemented by K-means clustering algorithm, due to its simplicity and speed. The optimization process was done by a well-known metaheuristic algorithms Grey Wolf Optimizer (GWO) and Cuckoo Search Optimizer. GWO is a metaheuristic algorithm inspired by the social hierarchy and hunting behavior of grey wolves. It mimics the leadership hierarchy and hunting strategies of wolves to explore the search space efficiently. GWO has shown promising performance in finding high-quality solutions compared to other well-established optimizers. It explores the solution space to find better cluster assignments that minimize the overall intra-cluster variance. By leveraging the exploration potential of GWO, the proposed algorithm aims to improve the quality of the clustering results. Furthermore, the Cuckoo Search Optimizer (CS) is combined with GWO to enhance the algorithm's ability to find a global solution. Cuckoo Search is a metaheuristic algorithm inspired by the breeding behavior of cuckoo birds. It employs random search and Levy flights to diversify the search process and avoid getting trapped in local optima. By combining CS with GWO, the proposed algorithm aims to increase the likelihood of finding the optimal clustering solution.