Clustering Performance Research Articles

Density Peak Clustering Algorithm Based on Shared Neighbors and Natural Neighbors and Analysis of Electricity Consumption Patterns

ABSTRACTThe Density Peaks Clustering (DPC) algorithm is well‐known for its simplicity and efficiency in clustering data of arbitrary shapes. However, it faces challenges such as inconsistent local density definitions and sample assignment errors. This paper introduces the Shared Neighbors and Natural Neighbors Density Peaks Clustering (SN‐DPC) algorithm to address these issues. SN‐DPC redefines local density by incorporating weighted shared neighbors, which enhances the density contribution from distant samples and provides a better representation of the data distribution. It also establishes a new similarity measure between samples using shared and natural neighbors, which increases intra‐cluster similarity and reduces assignment errors, thereby improving clustering performance. Compared with DPC‐CE, IDPC‐FA, DPCSA, FNDPC, and traditional DPC, SN‐DPC demonstrated superior effectiveness on both synthetic and real datasets. When applied to the analysis of electricity consumption patterns, it more accurately identified load consumption patterns and usage habits.

Mining functional gene modules by multi-view NMF of phenome-genome association

BackgroundMining functional gene modules from genomic data is an important step to detect gene members of pathways or other relations such as protein-protein interactions. This work explores the plausibility of detecting functional gene modules by factorizing gene-phenotype association matrix from the phenotype ontology data rather than the conventionally used gene expression data. Recently, the hierarchical structure of phenotype ontologies has not been sufficiently utilized in gene clustering while functionally related genes are consistently associated with phenotypes on the same path in phenotype ontologies.ResultsThis work demonstrates a hierarchical Nonnegative Matrix Factorization (NMF) framework, called Consistent Multi-view Nonnegative Matrix Factorization (CMNMF), which factorizes genome-phenome association matrix at consecutive levels of the hierarchical structure in phenotype ontology to mine functional gene modules. CMNMF constrains the gene clusters from the association matrices at two consecutive levels to be consistent since the genes are annotated with both the child-level phenotypes and the parent-level phenotypes in two levels. CMNMF also restricts the identified gene clusters to be densely connected in the phenotype ontology hierarchy. In the experiments on mining functionally related genes from mouse phenotype ontology and human phenotype ontology, CMNMF effectively improves clustering performance over the baseline methods. Gene ontology enrichment analysis is also conducted to verify its practical effectiveness to reveal meaningful gene modules.ConclusionsUtilizing the information in the hierarchical structure of phenotype ontology, CMNMF can identify functional gene modules with more biological significance than conventional methods. CMNMF can also be a better tool for predicting members of gene pathways and protein-protein interactions.

UNCA: A Neutrosophic-Based Framework for Robust Clustering and Enhanced Data Interpretation

Accurately representing the complex linkages and inherent uncertainties included in huge datasets is still a major difficulty in the field of data clustering. We address these issues with our proposed Unified Neutrosophic Clustering Algorithm (UNCA), which combines a multifaceted strategy with Neutrosophic logic to improve clustering performance. UNCA starts with a full-fledged similarity examination via a λ-cutting matrix that filters meaningful relationships between each two points of data. Then, we initialize centroids for Neutrosophic K-Means clustering, where the membership values are based on their degrees of truth, indeterminacy and falsity. The algorithm then integrates with a dynamic network visualization and MST (Minimum Spanning Tree) so that a visual interpretation of the relationships between the clusters can be clearly represented. UNCA employs Single-Valued Neutrosophic Sets (SVNSs) to refine cluster assignments, and after fuzzifying similarity measures, guarantees a precise clustering result. The final step involves solidifying the clustering results through defuzzification methods, offering definitive cluster assignments. According to the performance evaluation results, UNCA outperforms conventional approaches in several metrics: it achieved a Silhouette Score of 0.89 on the Iris Dataset, a Davies-Bouldin Index of 0.59 on the Wine Dataset, an Adjusted Rand Index (ARI) of 0.76 on the Digits Dataset, and a Normalized Mutual Information (NMI) of 0.80 on the Customer Segmentation Dataset. These results demonstrate how UNCA enhances interpretability and resilience in addition to improving clustering accuracy when contrasted with Fuzzy C-Means (FCM), Neutrosophic C-Means (NCM), as well as Kernel Neutrosophic C-Means (KNCM). This makes UNCA a useful tool for complex data processing tasks.

Simulation and optimization study on coupled heat transfer performance among multi-layered geothermal buried pipe systems

ABSTRACT In the application of geothermal ground source heat pump technology at different depths, multiple ground heat exchangers are mainly used to provide heating for buildings. In order to study the coupled heat transfer performance between different depth regions of geothermal ground heat exchanger arrays, based on typical geological distributions, numerical models of geothermal ground heat exchanger arrays in different depth regions were constructed and validated against actual operational data, with an error range of 0.5% to 9.6%, basically meeting engineering accuracy. On this basis, the thermal interaction and temperature of each ground heat exchanger under different spacing, distribution, and soil thermal properties were studied. The results show that linear arrays exhibit obvious cooling accumulation benefits within the depth range of 0.6–2.2 m, square arrays exhibit obvious cooling accumulation benefits within the depth range of 0.6–2.6 m, and zigzag arrays exhibit obvious cooling accumulation benefits within the depth range of 0.6–1.8 m. Between 0.6–3.4 m, the unit area heat transfer of zigzag arrays is significantly lower compared to linear and square arrangements, with average reductions of 110.38% and 89.35%, respectively. The study systematically elucidates the impact of cold accumulation on the heat transfer performance of pipe clusters under different arrangements and depths of burial in geological structures.The conclusion drawn is that in the engineering design, deep-buried pipe clusters should be arranged as straight as possible. The value of this research lies in its systematic exploration of the impact of geothermal temperature variations on the heat transfer performance of ground source heat pump systems with different burial depths and pipe arrangements. It addresses a gap in the current understanding of “cold accumulation” phenomena in the design and optimization of heat exchangers, providing a more comprehensive theoretical foundation for improving system performance. These research findings provide theoretical guidance and important references for addressing the impact of “cold accumulation” between pipe clusters in subsequent engineering projects.

Facie classification of X-field in the Niger Delta using machine learning

The Niger Delta's complex geology presents challenges for accurate facies classification and reservoir characterization. This study applies machine learning techniques to well log data from four wells in the X-Field: Freeman-001, Freeman-003ST1, Freeman-004ST1, and Freeman-005. Key features analyzed include Gamma Ray (GR), Sonic Transient Time (DT), Bulk Density (RHOB), and Neutron Porosity (NPHI). Unsupervised learning methods (K-means, GMM, and Agglomerative Clustering) yielded moderate clustering performance, with K-means achieving the highest accuracy of 47.87%, followed by GMM (20.38%) and Agglomerative Clustering (13.94%). Supervised learning methods significantly outperformed unsupervised ones. Random Forest achieved 87.06% accuracy, 87.07% precision, and an F1-score of 87.06%. Gradient Boosting delivered the best results, with 87.53% accuracy, 87.55% precision, and an F1-score of 87.54%. SVM showed lower performance, with 77.86% accuracy and an F1-score of 77.82%. These findings highlight the superior performance of supervised learning, particularly tree-based models, in facies classification and underscore the potential of machine learning to enhance reservoir characterization in the Niger Delta.

Enhancing K-means Clustering Performance with a Two-Stage Hybrid Preprocessing Strategy

Enhancing K-means Clustering Performance with a Two-Stage Hybrid Preprocessing Strategy

Enhancing data curation with spectral clustering and Shannon entropy: An unsupervised approach within the data washing machine

In the realm of digital data proliferation, effective data curation is pivotal for extracting meaningful insights. This study explores the integration of spectral clustering and Shannon Entropy within the Data Washing Machine (DWM), a sophisticated tool designed for unsupervised data curation. Spectral clustering, known for its ability to handle complex and non-linearly separable data, is investigated as an alternative clustering method to enhance the DWM’s capabilities. Shannon Entropy is employed as a metric to evaluate and refine the quality of clusters, providing a measure of information content and homogeneity. The research involves rigorous testing of the DWM prototype on diverse datasets, assessing the performance of spectral clustering in conjunction with Shannon Entropy. Results indicate that spectral clustering, when combined with entropy-based evaluation, significantly improves clustering outcomes, particularly in datasets exhibiting varied density and complexity. This study highlights the synergistic role of spectral clustering and Shannon Entropy in advancing unsupervised data curation, offering a more nuanced approach to handling diverse data landscapes.

Features of the development and functioning of tourist clusters

The article presents comparative characteristics of tourism and industrial clusters in current business conditions based on the following criteria: cluster core, marketing and promotion, quality standards and production technology, cluster subjects, cluster performance.

Possibilistic C-means with novel image representation for image segmentation

Image segmentation is the process of automatically dividing an image into several parts and extracting the relevant data and information. Compared to the traditional Fuzzy C-Means algorithm, the Possibilistic C-Means (PCM) algorithm has advantages in reducing the influence of noise on cluster center estimation. However, the PCM algorithm still shows poor clustering performance under high-intensity noise, which may lead to overlapping cluster centers. Considering the impact of neighborhood information of image pixels on the image segmentation results, this paper proposes a Vector-Based Possibilistic C-Means (VBPCM) algorithm. The algorithm incorporates neighborhood information and uses a vector representation method to describe image pixels. Additionally, an adjustable distance based on an exponential function is proposed to describe the similarity between vectors. The proposed VBPCM algorithm outperforms the conventional PCM, obtaining uplifiting gains of 4%, 2%, and 9% in Pixel Accuracy, Mean Pixel Accuracy, and Mean Intersection over Union, respectively. The experimental outputs illustrate that VBPCM algorithm can achieve more satisfactory cluster effect with high-intensity noise, further perform better in image segmentation task.

Adaptive micro partition and hierarchical merging for accurate mixed data clustering

Heterogeneous attribute data (also called mixed data), characterized by attributes with numerical and categorical values, occur frequently across various scenarios. Since the annotation cost is high, clustering has emerged as a favorable technique for analyzing unlabeled mixed data. To address the complex real-world clustering task, this paper proposes a new clustering method called Adaptive Micro Partition and Hierarchical Merging (AMPHM) based on neighborhood rough set theory and a novel hierarchical merging mechanism. Specifically, we present a distance metric unified on numerical and categorical attributes to leverage neighborhood rough sets in partitioning data objects into fine-grained compact clusters. Then, we gradually merge the current most similar clusters to avoid incorporating dissimilar objects into a similar cluster. It turns out that the proposed approach breaks through the clustering performance bottleneck brought by the pre-set number of sought clusters k and cluster distribution bias, and is thus capable of clustering datasets comprising various combinations of numerical and categorical attributes. Extensive experimental evaluations comparing the proposed AMPHM with state-of-the-art counterparts on various datasets demonstrate its superiority.

A modified and weighted Gower distance-based clustering analysis for mixed type data: a simulation and empirical analyses

BackgroundTraditional clustering techniques are typically restricted to either continuous or categorical variables. However, most real-world clinical data are mixed type. This study aims to introduce a clustering technique specifically designed for datasets containing both continuous and categorical variables to offer better clustering compatibility, adaptability, and interpretability than other mixed type techniques.MethodsThis paper proposed a modified Gower distance incorporating feature importance as weights to maintain equal contributions between continuous and categorical features. The algorithm (DAFI) was evaluated using five simulated datasets with varying proportions of important features and real-world datasets from the 2011–2014 National Health and Nutrition Examination Survey (NHANES). Effectiveness was demonstrated through comparisons with 13 clustering techniques. Clustering performance was assessed using the adjusted Rand index (ARI) for accuracy in simulation studies and the silhouette score for cohesion and separation in NHANES. Additionally, multivariable logistic regression estimated the association between periodontitis (PD) and cardiovascular diseases (CVDs), adjusting for clusters in NHANES.ResultsIn simulation studies, the DAFI-Gower algorithm consistently performs better than baseline methods according to the adjusted Rand index in settings investigated, especially on datasets with more redundant features. In NHANES, 3,760 people were analyzed. DAFI-Gower achieves the highest silhouette score (0.79). Four distinct clusters with diverse health profiles were identified. By incorporating feature importance, we found that cluster formations were more strongly influenced by CVD-related factors. The association between periodontitis and cardiovascular diseases, after adjusting for clusters, reveals significant insights (adjusted OR 1.95, 95% CI 1.50 to 2.55, p = 0.012), highlighting severe periodontitis as a potential risk factor for cardiovascular diseases.ConclusionsDAFI performed better than classic clustering baselines on both simulated and real-world datasets. It effectively captures cluster characteristics by considering feature importance, which is crucial in clinical settings where many variables may be similar or irrelevant. We envisage that DAFI offers an effective solution for mixed type clustering.

Manipulation and Theoretical Modeling of the Luminescence Performance of Silver Clusters in LTA Zeolites by Adjusting Extra-framework Cations.

Luminescent silver clusters that are confined inside porous zeolites have attracted great research interest due to the combined advantages of high quantum yield, tunable emission, excellent chemical stability, and desirable adoption ability of this micronano-composite. In this research, a series of Ag-exchanged R-LTA/Ag (R = Li, Na, K) zeolites were synthesized, in which the silver clusters showed blue-shifted excitation in the UV region, red-shifted emission in the visible region, and decreased emission intensity when taking Li+, Na+, and K+ as the extra-framework cations, respectively. The [Ag4(H2O)xR4-sod]6- (x = 2, 4; R = Li, Na, K) structures were constructed, and the TD-DFT method was further carried out to study the influence of extra-framework cations on the luminescence performance of [Ag4]2+ clusters, in which the substitution of Al for Si and the reasonable hydrate levels were both taken into consideration. It is estimated that the S1 and S3 levels for the [Ag4(H2O)4Li4-sod]6- cluster, the S4 level for the [Ag4(H2O)4Na4-sod]6- cluster, and the S5 level for the [Ag4(H2O)2K4-sod]6- cluster can be effectively populated through direct excitation, and the S1 → S0 transition is assumed to be responsible for the radiative emission in the visible region for the [Ag4]2+ pseudo-tetrahedrons. This calculated energy level diagram and simulated absorption spectra of [Ag4(H2O)xR4-sod]6- (x = 2, 4; R = Li, Na, K) clusters explained the modified luminescence property of silver clusters in R-LTA/Ag (R = Li, Na, K) zeolites studied in this research and reported literature.

One Receptor Show: Evaluation of Feature Extraction Methods for a Single Gas Sensor Based on α-Tocopherol-Substituted Zinc Phthalocyanine.

A consistent part of gas sensor research activities aims to improve sensing performances by synthesizing new sensing materials, improving the selection of elements in arrays, and optimizing the feature extraction and classification algorithms. This paper combines most of these aspects to confer selectivity to a low-selectivity sensor by using feature extraction algorithms applied to the sensor response kinetics. Several algorithms were employed to represent the kinetic behavior of the sensor response during the adsorption and desorption phases. In detail, we utilized fitting functions, dynamic descriptors, and a mirrored fast Fourier transform (FFT) to extrapolate information from the desorption and adsorption phases. As sensitive material, we synthesized as a receptor a novel Zn(II) phthalocyanine derivative bearing long aliphatic chains, namely, 4-[2,5,7,8-tetramethyl-2-(4,8,12-trimethyl-tridecyl)-chroman-6-yloxy] units that allow good solubilization of the complex in most of the solvents tested and confer a large set of sites to potentially interact with volatile compound (VC) analytes by different portions of the molecular skeleton. After deposition onto a quartz crystal microbalance (QMB) transducer, this single sensor has been tested to recognize five VCs as chemical probes of different chemical families. Remarkably, the results disclose that responses at equilibrium are mainly correlated with nonspecific interactions, while the kinetic parameters are more correlated with stronger and more specific interactions. Finally, the desorption phases bear a higher information content about the chemical identities of the compounds. Eventually, features extracted by a modified version of the FFT obtained the highest clustering (principal component analysis) and classification performances (95% classification accuracy with linear discriminant analysis), suggesting the possibility of developing virtual electronic noses based on a single sensor and a minimal set of descriptors.

Improve Threshold Range of Canopy Clustering Using Optimization Algorithms

Canopy clustering is an effective method for determining the number of clusters dynamically without requiring a predefined cluster count, making it particularly suitable for large and complex datasets. However, its performance is highly dependent on the manual tuning of threshold parameters T1 and T2, which can be time-consuming and inefficient. This study aims to enhance the Canopy clustering algorithm by automating the optimization of threshold ranges using intelligent optimization algorithms. We propose a novel framework that integrates Simulated Annealing (SA), Particle Swarm Optimization (PSO), and Snake Optimization (SO) to automatically determine the optimal values of T1 and T2. Additionally, to address high-dimensional data complexity, we employ dimensionality reduction techniques such as t-SNE, SNE, and Kernel Principal Component Analysis (KPCA). The silhouette coefficient is utilized as the fitness function to evaluate clustering performance. Comprehensive experiments conducted on the Wine, Iris, and MNIST Subset datasets demonstrate that the proposed optimization-based Canopy clustering framework significantly improves clustering accuracy by up to 21% on the Wine dataset and 19% on the Iris dataset compared to traditional methods. Specifically, on the Wine dataset, the optimized Canopy clustering achieved a silhouette coefficient of 0.63, a 21% improvement over the original 0.52. On the Iris dataset, the optimized method outperformed k-means and manual Canopy clustering with silhouette coefficients of 0.62 versus 0.52 and 0.55, respectively. These results highlight the effectiveness of intelligent optimization algorithms in enhancing clustering adaptability and efficiency.

Enhancing Clustering Stability and Efficiency: A Framework for Optimizing K-means, K-medoids, and K-shape with Intelligent Algorithms

Clustering methods like Kmeans often produce inconsistent results due to the random initialization of cluster centroids, even with optimizations such as Kmeans++, which improve centroid selection but fail to eliminate sensitivity to initialization randomness. Additionally, the choice of the number of clusters and distance metrics significantly impacts clustering performance. This paper proposes an enhanced framework combining intelligent optimization algorithms—Sparrow Search Algorithm (SSA), Dung Beetle Optimizer (DBO), and Sine Cosine Algorithm (SCA)—to optimize clustering outcomes for Kmeans, Kmedoids, and Kshape. The framework also incorporates dimensionality reduction techniques, including Principal Component Analysis (PCA), Non-Negative Matrix Factorization (NNMF), and Singular Value Decomposition (SVD), to address high-dimensional data challenges. Experimental results on benchmark datasets demonstrate the proposed framework’s effectiveness, with SSA achieving the highest silhouette score of 0.68 and reducing runtime by 35% compared to traditional methods. This approach enhances clustering stability and accuracy, offering a robust solution for diverse applications.

Ferrocene-Functionalized Atomically Precise Metal Clusters Exhibit Synergistically Enhanced Performance for CO2 Electroreduction.

The integration of organometallic compounds with metal nanoparticles can, in principle, generate hybrid nanocatalysts endowed with augmented functionality, presenting substantial promise for catalytic applications. Herein, we synthesize an atomically precise metal cluster (Ag9Cu6) catalyst integrated with alkynylferrocene molecules (Ag9Cu6-Fc). This hybrid catalyst design facilitates a continuous electron transfer channel via an ethynyl bridge and establishes a distinctive local chemical environment, resulting in remarkably enhanced catalytic activity in CO2 electroreduction. The Ag9Cu6-Fc catalyst achieves a record-high product selectivity of CO Faradaic efficiency of 100 % and an industrial-level CO partial current density of -680 mA/cm2, surpassing the performance of the Ag9Cu6 cluster (62 % and -230 mA/cm2, respectively) without ferrocene functionalization in a membrane electrode assembly cell. Operando experimental and computational findings offer valuable insights into the role of ferrocene functionalization in synergistically improving the catalytic performance of metal clusters, propelling the advancement of metallic-organometallic hybrid nanoparticles for energy conversion technologies.

Graph Joint Representation Clustering via Penalized Graph Contrastive Learning.

Graph clustering based on graph contrastive learning (GCL) is one of the dominant paradigms in the current graph clustering research field. However, those GCL-based methods often yield false negative samples, which can distort the learned representations and limit clustering performance. In order to alleviate this issue, we propose the idea of maintaining mutual information (MI) between the representations and the inputs to mitigate the loss of semantic information of false negative samples. We demonstrate the validity of this proposal through relevant experiments. Since maximizing MI can be approximately replaced by minimizing reconstruction error, we further propose a graph clustering method based on GCL penalized by reconstruction error, in which our carefully designed reconstruction decoder, as well as reconstruction error term, improve the clustering performance. In addition, we use a pseudo-label-guided strategy to improve the GCL process and further alleviate the problem of false negative samples. Our experiment results demonstrate the superiority and great potential of our proposed graph clustering method compared with state-of-the-art algorithms.

Functional Concurrent Regression Mixture Models Using Spiked Ewens-Pitman Attraction Priors.

Functional concurrent, or varying-coefficient, regression models are a form of functional data analysis methods in which functional covariates and outcomes are collected concurrently. Two active areas of research for this class of models are identifying influential functional covariates and clustering their relations across observations. In various applications, researchers have applied and developed methods to address these objectives separately. However, no approach currently performs both tasks simultaneously. In this paper, we propose a fully Bayesian functional concurrent regression mixture model that simultaneously performs functional variable selection and clustering for subject-specific trajectories. Our approach introduces a novel spiked Ewens-Pitman attraction prior that identifies and clusters subjects' trajectories marginally for each functional covariate while using similarities in subjects' auxiliary covariate patterns to inform clustering allocation. Using simulated data, we evaluate the clustering, variable selection, and parameter estimation performance of our approach and compare its performance with alternative spiked processes. We then apply our method to functional data collected in a novel, smartphone-based smoking cessation intervention study to investigate individual-level dynamic relations between smoking behaviors and potential risk factors.

A Novel and Effective Method to Directly Solve Spectral Clustering.

Spectral clustering has been attracting increasing attention due to its well-defined framework and excellent performance. However, most traditional spectral clustering methods consist of two separate steps: 1) Solving a relaxed optimization problem to learn the continuous clustering labels, and 2) Rounding the continuous clustering labels into discrete ones. The clustering results of the relax-and-discretize strategy inevitably result in information loss and unsatisfactory clustering performance. Moreover, the similarity matrix constructed from original data may not be optimal for clustering since data usually have noise and redundancy. To address these problems, we propose a novel and effective algorithm to directly optimize the original spectral clustering model, called Direct Spectral Clustering (DSC). We theoretically prove that the original spectral clustering model can be solved by simultaneously learning a weighted discrete indicator matrix and a structured similarity matrix whose connected components are equal to the number of clusters. Both of them can be used to directly obtain the final clustering results without any post-processing. Further, an effective iterative optimization algorithm is exploited to solve the proposed method. Extensive experiments performed on synthetic and real-world datasets demonstrate the superiority and effectiveness of the proposed method compared to the state-of-the-art algorithms.

Bitcoin Address Clustering Based on Change Address Improvement

Change address identification is one of the difficulties in bitcoin address clustering as an emerging social computing problem. Most of the current-related research only applies to certain specific types of transactions and faces the problems of low recognition rate and high false positive rate. We innovatively propose a clustering method based on multiconditional recognition of one-time change addresses and conduct experiments with on-chain bitcoin transaction data. The results show that the proposed method identifies at least 12.3% more one-time change addresses than other heuristics. On top of the multi-input heuristic clustering method, the proposed method also improves the address clustering performance by 5.7%, achieves optimal recognition results compared with similar methods, and significantly reduces the false positive rate of recognition results. This work provides the technical basis for antimoney laundering efforts based on entity identification. Code and data could be accessed from https://github.com/ECNU-Cross-Innovation-Lab/BitcoinAddressClustering.