Clustering Accuracy Research Articles

Parameter extension of q-divergence-based fuzzy c-means clustering

AbstractIn this study, we propose a fuzzy clustering algorithm for vectorial data, which is constructed by extending the fuzzification parameters in the q-divergence-based fuzzy c-means algorithm (QFCM). The proposed algorithm, referred to as extended QFCM (eQFCM), is an extension of both QFCM and the penalized fuzzy c-means algorithm proposed by Yang, referred to as Y-type FCM (YFCM). eQFCM extends both the two-parameter QFCM and YFCM algorithms to a four-parameter model. Through numerical experiments using an artificial dataset, we substantiate the theoretical discussion, and the effects of fuzzification parameter to clustering results are observed. Furthermore, the results of some numerical experiments using real datasets are presented to demonstrate that the proposed algorithm outperformed both QFCM and YFCM algorithms in terms of clustering accuracy.

A New Clustering Accuracy Measure Based on Relative Distances and its Cross-Validation Using Dirichlet Distribution

A New Clustering Accuracy Measure Based on Relative Distances and its Cross-Validation Using Dirichlet Distribution

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.

A Natural Language Processing Model for Automated Organization and Analysis of Intangible Cultural Heritage

This paper investigates text similarity methods in the field of NLP, improves upon the WMD, and develops the SWC-WMD distance, forming the basis for a clustering method for long ICH texts. Clustering experiments on the constructed ICH long text dataset using WMD, SWC-WMD, and TF-IDF-WMD distances were conducted. The impact of the number of feature words on clustering results and the effect of different distances on clustering outcomes were assessed based on accuracy and F1 values from the evaluation criteria. The final results show that the SWC-WMD distance improves the accuracy and F1 values of the ICH long text clustering results compared to the other two distances, thereby proving the effectiveness of the methods proposed in this paper.

Industry Class Clustering of Software Expertise Competency at SMKN 2 Kraksaan Using Constrained K-Means Clustering Algorithm

Addressing the gap between school education and industry needs is a recurring concern, as many graduates struggle to enter the workforce due to lacking practical skills. Industry Classes aim to bridge this gap by preparing students with relevant skills and knowledge aligned with real-world industry demands. This study focuses on the application of Constrained K-Means Clustering to categorize students in the software engineering competency classes at SMKN 2 Kraksaan. This algorithm modifies traditional K-Means by utilizing Linear Programming Algorithm (LPA), ensuring each cluster meets predefined subject requirements. The research involves analyzing academic proficiency test data (TKDA) from 96 X-grade students, evaluating their abilities in analogy, series, figural, mathematical, and recall skills. Using a 3-cluster approach, each with 32 to 60 student capacity constraints, the study aims to optimize student distribution for effective learning outcomes. Evaluation through silhouette method yielded a score of 0.3199, indicating satisfactory separation between clusters with overlap to address. Cluster analysis revealed Cluster 2 as the most proficient, showcasing strengths in recall and series attributes critical for software engineering. These findings suggest that Constrained K-Means Clustering is effective in classifying students, highlighting Cluster 2 as optimal for software engineering competencies at SMKN 2 Kraksaan. Future research should focus on enhancing data quality, expanding sample size, and refining algorithms for improved clustering accuracy and effectiveness.

Grouping Districts / Cities in Central Sulawesi Province Based on Poverty Indicators Using the Fuzzy Geographically Weighted Clustering -Artificial Bee Colony Method

Introduction: Poverty is the main problem that is the focus of attention of the government in Indonesia. In general, poverty is a person's inability to meet basic basic needs in every aspect of life. Cluster analysis is a solution to map this problem. Method: Fuzzy Geographically Weighted Clustering-Artificial Bee Colony (FGWC-ABC) is one clustering method that is an integration of classical fuzzy clustering methods and geodemographic elements. Artificial Bee Colony is a metaheuristic algorithm that is used as a global optimization to increase cluster accuracy. Artificial Bee Colony can efficiently and effectively solve various function optimization problems in various cases. Result and Discussion: The research results obtained 3 optimum clusters with each cluster characteristic relatively different based on poverty indicators. Cluster 1 with low poverty, cluster 2 with high poverty, and cluster 3 with moderate poverty. Conclusion: By using the IFV validity index, 3 optimum clusters were obtained with different characteristics of each cluster based on its indicators. Cluster 1 consists of three regencies/cities with low poverty status, cluster 2 consists of seven regencies/cities with high poverty status, and cluster 3 consists of six regencies/cities with moderate poverty status.

Global Mean Based nearest Feature object Value Selection with Feature creation Method for Clustering Accuracy Improvement

Global Mean Based nearest Feature object Value Selection with Feature creation Method for Clustering Accuracy Improvement

SmartImpute: A Targeted Imputation Framework for Single-cell Transcriptome Data.

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity and tissue transcriptomic complexity. However, the high frequency of dropout events in scRNA-seq data complicates downstream analyses such as cell type identification and trajectory inference. Existing imputation methods address the dropout problem but face limitations such as high computational cost and risk of over-imputation. We present SmartImpute, a novel computational framework designed for targeted imputation of scRNA-seq data. SmartImpute focuses on a predefined set of marker genes, enhancing the biological relevance and computational efficiency of the imputation process while minimizing the risk of model misspecification. Utilizing a modified Generative Adversarial Imputation Network architecture, SmartImpute accurately imputes the missing gene expression and distinguishes between true biological zeros and missing values, preventing overfitting and preserving biologically relevant zeros. To ensure reproducibility, we also provide a function based on the GPT4 model to create target gene panels depending on the tissue types and research context. Our results, based on scRNA-seq data from head and neck squamous cell carcinoma and human bone marrow, demonstrate that SmartImpute significantly enhances cell type annotation and clustering accuracy while reducing computational burden. Benchmarking against other imputation methods highlights SmartImpute's superior performance in terms of both accuracy and efficiency. Overall, SmartImpute provides a lightweight, efficient, and biologically relevant solution for addressing dropout events in scRNA-seq data, facilitating deeper insights into cellular heterogeneity and disease progression. Furthermore, SmartImpute's targeted approach can be extended to spatial omics data, which also contain many missing values.

A particle swarm optimization-based deep clustering algorithm for power load curve analysis

To address the inflexibility of the convolutional autoencoder (CAE) in adjusting the network structure and the difficulty of accurately delineating complex class boundaries in power load data, a particle swarm optimization deep clustering method (DC-PSO) is proposed. First, a particle swarm optimization algorithm for automatically searching the optimal network architecture and hyperparameters of CAE (AHPSO) is proposed to obtain better reconstruction performance. Then, an end-to-end deep clustering model based on a reliable sample selection strategy is designed for the deep clustering algorithm to accurately delineate the category boundaries and further improve the clustering effect. The experimental results show that the DC-PSO algorithm exhibits high clustering accuracy and higher performance for the power load profile clustering.

Trisection-fusion and fusion-trisection methods of three-way conflict analysis with Pythagorean fuzzy information

A basic task of Pawlak conflict analysis is to cluster agents based on their attitudes towards various issues. When ratings of agents are imprecise and uncertain, for example, as represented by a Pythagorean fuzzy situation table, single-measure based methods fall short in ensuring the accuracy of agent clusters. Multi-measure based conflict analysis methods are more effective for generating higher-quality agent clusters. The objective of this paper is to propose two new multi-measure based methods under uncertainty represented by Pythagorean fuzzy sets. We introduce three distinct conflict measures regarding an issue by leveraging the maximum positive and negative agents. The first measure is based on support degrees, the second measure incorporates opposition degrees, and the third measure considers both support and opposition degrees. These measures trisect a set of agents into three disjoint coalitions concerning an individual issue. For multiple issues, we propose two models by combing the two fundamental tasks, namely, trisection and fusion. A trisection-fusion model amalgamates a family of trisections generated from multiple issues based on conflict measures regarding single issues. Matrix representations of trisections are provided, and trisection fusions are transformed into a series of matrix operations. A fusion-trisection model trisects the set of agents according to fused conflict measures on multiple issues. To demonstrate the value of the two models, we apply them in assisting decision-makers in Hunan Province, China, to adjust development plans. The experimental results show that multi-measure based models offer decision-makers more comprehensive guidance.

Late Fusion Multiview Clustering via Min-Max Optimization.

Multiview clustering (MVC) sufficiently exploits the diverse and complementary information among different views to improve the clustering performance. As a representative algorithm of MVC, the newly proposed simple multiple kernel k -means (SimpleMKKM) algorithm takes a min-max formulation and applies a gradient descent algorithm to decrease the resultant objective function. It is empirically observed that its superiority is attributed to the novel min-max formulation and the new optimization. In this article, we propose to integrate the min-max learning paradigm adopted by SimpleMKKM into late fusion MVC (LF-MVC). This leads to a tri-level max-min-max optimization problem with respect to the perturbation matrices, weight coefficient, and clustering partition matrix. To solve this intractable max-min-max optimization problem, we design an efficient two-step alternative optimization strategy. Furthermore, we analyze the generalization clustering performance of the proposed algorithm from the theoretical perspective. Comprehensive experiments have been conducted to evaluate the proposed algorithm in terms of clustering accuracy (ACC), computation time, convergence, as well as the evolution of the learned consensus clustering matrix, clustering with different numbers of samples, and analysis of the learned kernel weight. The experimental results show that the proposed algorithm is able to significantly reduce the computation time and improve the clustering ACC when compared to several state-of-the-art LF-MVC algorithms. The code of this work is publicly released at: https://xinwangliu.github.io/Under-Review.

An interpretable Bayesian clustering approach with feature selection for analyzing spatially resolved transcriptomics data.

Recent breakthroughs in spatially resolved transcriptomics (SRT) technologies have enabled comprehensive molecular characterization at the spot or cellular level while preserving spatial information. Cells are the fundamental building blocks of tissues, organized into distinct yet connected components. Although many non-spatial and spatial clustering approaches have been used to partition the entire region into mutually exclusive spatial domains based on the SRT high-dimensional molecular profile, most require an ad hoc selection of less interpretable dimensional-reduction techniques. To overcome this challenge, we propose a zero-inflated negative binomial mixture model to cluster spots or cells based on their molecular profiles. To increase interpretability, we employ a feature selection mechanism to provide a low-dimensional summary of the SRT molecular profile in terms of discriminating genes that shed light on the clustering result. We further incorporate the SRT geospatial profile via a Markov random field prior. We demonstrate how this joint modeling strategy improves clustering accuracy, compared with alternative state-of-the-art approaches, through simulation studies and 3 real data applications.

Density peaks clustering based on Gaussian fuzzy neighborhood with noise parameter

Density peak clustering (DPC) is an effective clustering method known for its robustness, non-iterative nature, and hybrid approach. However, it is not without limitations: (a) the determination of the cutoff distance (dc) relies on human experience, which can significantly impact the clustering outcome; (b) DPC does not take into account the local structure of the data when computing local densities; (c) it employs a crisp kernel for density computation; (d) the performance of DPC is affected by chain reactions; and (e) DPC often struggles to handle noisy data. In order to address these limitations, this paper proposes a novel approach called DPC based on Gaussian fuzzy neighborhood with noise parameter (DPC-GFNN). The proposed method leverages a Gaussian fuzzy kernel to enhance the separation between clusters and mitigates the influence of outliers through an adjustable noise parameter (λ). DPC-GFNN utilizes a k-nearest neighbor graph based on density to label highly dense regions. This technique effectively avoids chain reactions by assigning accurate labels to points in border areas, enabling proper clustering of data with diverse shapes and densities. To evaluate the effectiveness of DPC-GFNN, a series of experiments are conducted on both real-world and synthetic datasets. The experimental results demonstrate that DPC-GFNN exhibits superior robustness and clustering accuracy compared to other modified variants of DPC including DPC based on k-nearest neighbors (DPC-KNN), improved DPC (IDPC), DPC based on density backbone and fuzzy neighborhood (DPC-DBFN), and DPC based on fuzzy weighted k-nearest neighbors (FKNN).

Clustering Integrated Fusion Model Based on the Maximum Matching Problem

The maximum-weight perfect matching inverse issue in graph theory and text clustering are the two primary topics of this study. We suggest a novel approach to text clustering that makes use of self-encoders and BERT embeddings for feature extraction in order to increase clustering accuracy. According to experimental results, our technique enhances the clustering results greatly and performs well on numerous short text datasets. In the context of graph theory, we examine the unit paradigm inverse issue of maximum-weight perfect matching with value constraints and provide a robust polynomial-time method for its solution. In addition to effectively solving the maximum-weight perfect matching inverse issue, our technique can also produce the best weight vector configuration scheme for real-world uses. In conclusion, our work has advanced the domains of text clustering and graph theory significantly, offering fresh approaches and theoretical underpinnings for future investigations.

Explicit unsupervised feature selection based on structured graph and locally linear embedding

Numerous redundant and irrelevant features are usually contained in high-dimensional data whose presence, if ignored, can bring detrimental effects on the performance of data processing tasks. Unsupervised feature selection (UFS) as a trending topic in a great deal of domains has been greatly concerned, which can effectively filter out redundant and irrelevant features in the unlabeled data. The majority of existing UFS methods mainly focus on building the parameterized model in the original feature space or some certain low-dimensional feature subspace, which cannot fully capture the information of the target space. In this work, we propose a novel UFS method that unifies the explicit feature selective matrix and structured graph into a learning framework. An explicit selection matrix, whose scale, bound of the elements and structure are considered, is tailored for UFS. Furthermore, we take into account the local consistency so that the low dimensional manifold structures can be preserved to a better degree. Instead of using the pre-defined similarity matrix to construct graph, we make use of the way of learning to obtain the adaptive graph. Besides, for the purpose of exploiting the cluster structure of data, we impose a rank constraint on the graph. Notably, both the explicit selection matrix and intrinsic structure are learned in the target feature subspace, and thus the proposed methodology is more straightforward and effective. We present an effective and efficient algorithm for the resulting problem based on alternative optimization strategy, and establish its convergence and computational complexity analysis. The experimental results show that our proposed approach has improved by over 5.28% and 5.79% on average in terms of clustering accuracy (ACC) and Normalized Mutual Information (NMI) compared with comparison methods, which demonstrates its superiority.

Projected fuzzy [formula omitted]-means clustering algorithm with instance penalty

At present, high-dimensional data clustering has become a vital research field in machine learning. Traditional clustering algorithms cannot perform well on high-dimensional data, where the clustering task is usually divided into two stages: dimensionality reduction first and clustering later. In general, the existing high-dimensional clustering methods usually have the following shortcomings: (1) the two-stage strategy splits the connection between clustering and dimensionality reduction; (2) these algorithms do not consider the impact of anomalous instances in high-dimensional data on clustering performance. Therefore, to address these problems, a projected fuzzy c-means clustering algorithm with instance penalty (PCIP) is proposed. Firstly, we construct an instance penalty matrix and assign an instance penalty coefficient to each sample. Secondly, a model for clustering high-dimensional data is constructed by integrating fuzzy c-means clustering (FCM) and principal component analysis (PCA). The proposed model can perform dimensionality reduction and clustering simultaneously. In addition, the time complexity of the proposed algorithm is linearly related to the number of samples n, which can efficiently deal with large data sets. The proposed PCIP algorithm is verified by experiments using clustering accuracy and normalized mutual information (NMI) as evaluation metrics. The experimental results on 10 image datasets show that the average accuracy and average NMI of the PCIP algorithm are improved by 0.0375 and 0.0275, respectively, compared to the second-ranked algorithm.

The Origin of Young Stellar Populations in NGC 1783: Accretion of External Stars

The presence of young stellar populations in the Large Magellanic Cloud cluster NGC 1783 has caught significant attention, with suggestions ranging from it being a genuine secondary stellar generation to a population of blue straggler stars or simply contamination from background stars. Thanks to multiepoch observations with the Hubble Space Telescope, proper motions for stars within the field of NGC 1783 have been derived, thus allowing accurate cluster membership determination. Here, we report that the younger stars within NGC 1783 indeed belong to the cluster and their spatial distribution is more extended compared to the bulk of the older stellar population, consistent with previous studies. Through N-body simulations, we demonstrate that the observed characteristics of the younger stars cannot be explained solely by blue straggler stars in the context of the isolated dynamical evolution of NGC 1783. Instead, accretion of the external, low-mass stellar system can better account for both the inverse spatial concentration and the radial velocity isotropy of the younger stars. We propose that NGC 1783 may have accreted external stars from low-mass stellar systems, resulting in a mixture of external younger stars and blue straggler stars from the older bulk population, thereby accounting for the characteristics of the younger sequence.

A new approach for data clustering based on granular computing

This paper introduces a new clustering technique based on granular computing. In traditional clustering algorithms, the integration of the high shaping capability of the existing datasets becomes fussy which in turn results in inferior functioning. Furthermore, the laid-out technique will be able to avoid these challenges through the use of granular computing to bring in a more accurate and prompt clustering process. The creation of a novel algorithm hinges on utilizing granules, which are the information chunks that reveal a natural structure as part of the data and also help with natural clustering. A testing of the algorithm's features is carried out by using state-of-the-art datasets and then an algorithm's effectiveness is compared to the other clustering methods. The results of the experiment show significant improvement in clustering accuracy and reduction in data analysis time, thus testifying how granular computing is efficient in data analysis. This quest is not only going to serve as a reinforcement in data clustering, but it will also probably be an input in the broader area of unsupervised learning, reinforcing positions for scalable and interpretable solutions for data-driven decision-making.

Network Models of BACE-1 Inhibitors: Exploring Structural and Biochemical Relationships.

This study investigates the clustering patterns of human β-secretase 1 (BACE-1) inhibitors using complex network methodologies based on various distance functions, including Euclidean, Tanimoto, Hamming, and Levenshtein distances. Molecular descriptor vectors such as molecular mass, Merck Molecular Force Field (MMFF) energy, Crippen partition coefficient (ClogP), Crippen molar refractivity (MR), eccentricity, Kappa indices, Synthetic Accessibility Score, Topological Polar Surface Area (TPSA), and 2D/3D autocorrelation entropies are employed to capture the diverse properties of these inhibitors. The Euclidean distance network demonstrates the most reliable clustering results, with strong agreement metrics and minimal information loss, indicating its robustness in capturing essential structural and physicochemical properties. Tanimoto and Hamming distance networks yield valuable clustering outcomes, albeit with moderate performance, while the Levenshtein distance network shows significant discrepancies. The analysis of eigenvector centrality across different networks identifies key inhibitors acting as hubs, which are likely critical in biochemical pathways. Community detection results highlight distinct clustering patterns, with well-defined communities providing insights into the functional and structural groupings of BACE-1 inhibitors. The study also conducts non-parametric tests, revealing significant differences in molecular descriptors, validating the clustering methodology. Despite its limitations, including reliance on specific descriptors and computational complexity, this study offers a comprehensive framework for understanding molecular interactions and guiding therapeutic interventions. Future research could integrate additional descriptors, advanced machine learning techniques, and dynamic network analysis to enhance clustering accuracy and applicability.

Consensus Subspace Graph Regularization based on prior information for multiplex network clustering

Multiplex network clustering can identify the common cluster structure shared by all layers, which is of great significance for downstream research such as personalized recommendations of social relationships and the mining of social media communication behaviors. Traditional multiplex network clustering methods rely only on the topological structure and are suitable for networks with clear structures. However, real-world networks often suffer from sparse connectivity and noisy edges. In addition, the current methods based on prior information only use the obtained consensus prior information as a preprocessing method for multiplex networks, and do not fully utilize and integrate the prior information in the clustering process, resulting in a low accuracy. To solve the problem of insufficient utilization of prior information, we propose a Consensus Subspace Graph Regularization (CSGR) approach for multiplex network clustering, which integrates topological information with the consensus prior information of a network. We first construct the consensus prior matrix of a network by a non-overlapping greedy search method, which represents a subset of the network that consists of nodes with high edge density. Then we construct a graph regularization term, which encodes the consensus prior information, to optimize the generation of a joint low-dimensional representation within the consensus subspace for the entire multiplex network. Finally, we employ the consensus prior matrix to denoise each network layer, thereby yielding a precise low-dimensional representation of each layer for consensus subspace fusion. In addition, comprehensive experiments have demonstrated that CSGR improves the clustering accuracy of real-world networks by an average of 1.56%.