Clustering Accuracy Research Articles

Comparison of peripheral blood automated hematopoietic progenitor cell count and flow cytometric CD34+ cell count.

Stem cell apheresis in the context of autologous stem cell transplantation requires an accurate cluster of differentiantion 34 (CD34+) count determined by flow cytometry as the current gold standard. Since flow cytometry is a personnel and time-intensive diagnostic tool, automated stem cell enumeration may provide a promising alternative. Hence, this study aimed to compare automated hematopoietic progenitor enumeration carried out on a Sysmex XN-20 module compared with conventional flow cytometric measurements. One hundred forty-three blood samples from 41 patients were included in this study. Correlation between the two methods was calculated over all samples, depending on leukocyte count and diagnosis. Overall, we found a high degree of correlation (r = 0.884). Furthermore, correlation was not impaired by elevated leukocyte counts (>10 000/μL, r = 0.860 vs <10 000/μL, r = 0.849; >20 000/μL, r = 0.843 vs <20 000/μL, r = 0.875). However, correlation was significantly impaired in patients with multiple myeloma (multiple myeloma r = 0.840 vs nonmyeloma r = 0.934). Stem cell measurement carried out on the Sysmex XN-20 module provides a significant correlation with flow cytometry and might be implemented in clinical practice. In clinical decision-making, there was discrepancy of under 15% of cases. In multiple myeloma patients, XN-20 should be used with caution.

Uncover spatially informed variations for single-cell spatial transcriptomics with STew.

The recent spatial transcriptomics (ST) technologies have enabled characterization of gene expression patterns and spatial information, advancing our understanding of cell lineages within diseased tissues. Several analytical approaches have been proposed for ST data, but effectively utilizing spatial information to unveil the shared variation with gene expression remains a challenge. We introduce STew, a Spatial Transcriptomic multi-viEW representation learning method, to jointly analyze spatial information and gene expression in a scalable manner, followed by a data-driven statistical framework to measure the goodness of model fit. Through benchmarking using human dorsolateral prefrontal cortex and mouse main olfactory bulb data with true manual annotations, STew achieved superior performance in both clustering accuracy and continuity of identified spatial domains compared with other methods. STew is also robust to generate consistent results insensitive to model parameters, including sparsity constraints. We next applied STew to various ST data acquired from 10× Visium, Slide-seqV2, and 10× Xenium, encompassing single-cell and multi-cellular resolution ST technologies, which revealed spatially informed cell type clusters and biologically meaningful axes. In particular, we identified a proinflammatory fibroblast spatial niche using ST data from psoriatic skins. Moreover, STew scales almost linearly with the number of spatial locations, guaranteeing its applicability to datasets with thousands of spatial locations to capture disease-relevant niches in complex tissues. Source code and the R software tool STew are available from github.com/fanzhanglab/STew.

Characterizing efficient feature selection for single-cell expression analysis.

Unsupervised feature selection is a critical step for efficient and accurate analysis of single-cell RNA-seq data. Previous benchmarks used two different criteria to compare feature selection methods: (i) proportion of ground-truth marker genes included in the selected features and (ii) accuracy of cell clustering using ground-truth cell types. Here, we systematically compare the performance of 11 feature selection methods for both criteria. We first demonstrate the discordance between these criteria and suggest using the latter. We then compare the distribution of selected genes in their means between feature selection methods. We show that lowly expressed genes exhibit seriously high coefficients of variation and are mostly excluded by high-performance methods. In particular, high-deviation- and high-expression-based methods outperform the widely used in Seurat package in clustering cells and data visualization. We further show they also enable a clear separation of the same cell type from different tissues as well as accurate estimation of cell trajectories.

Deep Batch Integration and Denoise of Single-Cell RNA-Seq Data.

Numerous single-cell transcriptomic datasets from identical tissues or cell lines are generated from different laboratories or single-cell RNA sequencing (scRNA-seq) protocols. The denoising of these datasets to eliminate batch effects is crucial for data integration, ensuring accurate interpretation and comprehensive analysis of biological questions. Although many scRNA-seq data integration methods exist, most are inefficient and/or not conducive to downstream analysis. Here, DeepBID, a novel deep learning-based method for batch effect correction, non-linear dimensionality reduction, embedding, and cell clustering concurrently, is introduced. DeepBID utilizes a negative binomial-based autoencoder with dual Kullback-Leibler divergence loss functions, aligning cell points from different batches within a consistent low-dimensional latent space and progressively mitigating batch effects through iterative clustering. Extensive validation on multiple-batch scRNA-seq datasets demonstrates that DeepBID surpasses existing tools in removing batch effects and achieving superior clustering accuracy. When integrating multiple scRNA-seq datasets from patients with Alzheimer's disease, DeepBID significantly improves cell clustering, effectively annotating unidentified cells, and detecting cell-specific differentially expressed genes.

Designing meaningful continuous representations of T cell receptor sequences with deep generative models

T Cell Receptor (TCR) antigen binding underlies a key mechanism of the adaptive immune response yet the vast diversity of TCRs and the complexity of protein interactions limits our ability to build useful low dimensional representations of TCRs. To address the current limitations in TCR analysis we develop a capacity-controlled disentangling variational autoencoder trained using a dataset of approximately 100 million TCR sequences, that we name TCR-VALID. We design TCR-VALID such that the model representations are low-dimensional, continuous, disentangled, and sufficiently informative to provide high-quality TCR sequence de novo generation. We thoroughly quantify these properties of the representations, providing a framework for future protein representation learning in low dimensions. The continuity of TCR-VALID representations allows fast and accurate TCR clustering and is benchmarked against other state-of-the-art TCR clustering tools and pre-trained language models.

Multi-Objective Automatic Clustering Algorithm Based on Evolutionary Multi-Tasking Optimization

Data mining technology is the process of extracting hidden knowledge and potentially useful information from a large number of incomplete, noisy, and random practical application data. The clustering algorithm based on multi-objective evolution has obvious advantages compared with the traditional single-objective method. In order to further improve the performance of evolutionary multi-objective clustering algorithms, this paper proposes a multi-objective automatic clustering model based on evolutionary multi-task optimization. Based on the multi-objective clustering algorithm that automatically determines the value of k, evolutionary multi-task optimization is introduced to deal with multiple clustering tasks simultaneously. A set of non-dominated solutions for clustering results is obtained by concurrently optimizing the overall deviation and connectivity index. Multi-task adjacency coding based on a locus adjacency graph was designed to encode the clustered data. Additionally, an evolutionary operator based on relevance learning was designed to facilitate the evolution of individuals within the population. It also facilitates information transfer between individuals with different tasks, effectively avoiding negative transfer. Finally, the proposed algorithm was applied to both artificial datasets and UCI datasets for testing. It was then compared with traditional clustering algorithms and other multi-objective clustering algorithms. The results verify the advantages of the proposed algorithm in clustering accuracy and algorithm convergence.

Feasibility analysis of using CO2 as a building fire detection indicator

CO2 has rarely been examined as a fire indicator owing to the uncertainty in its background concentration. Nevertheless, for a specific building space with a detector, the background CO2 concentration signal can be determined and documented using artificial intelligence technology. Based on the aforementioned concept, this study explored the feasibility of using CO2 signal features as fire indicators for building fire detection. First, CO2 concentrations under fire and no-fire conditions were obtained to generate study databases. Second, the deep temporal clustering (DTC) model was used to classify CO2 signal features between fire and no-fire scenes, and the clustering accuracy and purity were selected to determine the clustering performance. Third, the k-means, agglomerative clustering, and spectral bi-clustering models were employed to validate the clustering advantages of the DTC model. The results proved the CO2 concentration to be more suitable as a combustion indicator for fire detection compared to the CO2 concentration rate, exhibiting higher clustering accuracy and purity values. Additionally, for time-series clustering, the DTC model outperformed traditional clustering models. The findings of this study represent a fundamental step toward the realization of space-exclusive multi-sensor fire-detection technology for assisting smart building construction in a cost-effective and accurate manner.

Multi-view and Multi-order Graph Clustering via Constrained [formula omitted]-norm

The graph-based multi-view clustering algorithms achieve decent clustering performance by consensus graph learning of the first-order graphs from different views. However, the first-order graphs are often sparse, lacking essential must-link information, which leads to suboptimal consensus graph. While high-order graphs can address this issue, a two-step strategy involving the selection of a fixed number of high-order graphs followed by their fusion may result in information loss or redundancy, restricting the exploration of high-order information. To address these challenges, we propose Multi-view and Multi-order Graph Clustering via Constrained l1,2-norm (MoMvGC), which mitigates the impact of graph sparsity on multi-view clustering. By innovatively designing constrained l1,2-norm, the model ingeniously integrates the selection of multi-order graphs and corresponding weight learning into a unified framework. Furthermore, MoMvGC not only enable sparse selection of multi-order graphs but also simultaneous selection of views. Afterwards, we design an efficient alternative optimization algorithm to solve the optimization problems in MoMvGC. The proposed model achieves state-of-the-art clustering performance on nine real-world datasets, with particularly notable improvements observed on the MSRC dataset, where the clustering accuracy is increased by 5.24% compared to the best baseline. Comprehensive experiments demonstrate the effectiveness and superiority of our model. The code is available at https://github.com/haonanxin/MoMvGC_code.

Design of health information management model for elderly care using an advanced higher-order hybrid clustering algorithm from the perspective of sports and medicine integration.

In the context of integrating sports and medicine domains, the urgent resolution of elderly health supervision requires effective data clustering algorithms. This paper introduces a novel higher-order hybrid clustering algorithm that combines density values and the particle swarm optimization (PSO) algorithm. Initially, the traditional PSO algorithm is enhanced by integrating the Global Evolution Dynamic Model (GEDM) into the Distribution Estimation Algorithm (EDA), constructing a weighted covariance matrix-based GEDM. This adapted PSO algorithm dynamically selects between the Global Evolution Dynamic Model and the standard PSO algorithm to update population information, significantly enhancing convergence speed while mitigating the risk of local optima entrapment. Subsequently, the higher-order hybrid clustering algorithm is formulated based on the density value and the refined PSO algorithm. The PSO clustering algorithm is adopted in the initial clustering phase, culminating in class clusters after a finite number of iterations. These clusters then undergo the application of the density peak search algorithm to identify candidate centroids. The final centroids are determined through a fusion of the initial class clusters and the identified candidate centroids. Results showcase remarkable improvements: achieving 99.13%, 82.22%, and 99.22% for F-measure, recall, and precision on dataset S1, and 75.22%, 64.0%, and 64.4% on dataset CMC. Notably, the proposed algorithm yields a 75.22%, 64.4%, and 64.6% rate on dataset S, significantly surpassing the comparative schemes' performance. Moreover, employing the text vector representation of the LDA topic vector model underscores the efficacy of the higher-order hybrid clustering algorithm in efficiently clustering text information. This innovative approach facilitates swift and accurate clustering of elderly health data from the perspective of sports and medicine integration. It enables the identification of patterns and regularities within the data, facilitating the formulation of personalized health management strategies and addressing latent health concerns among the elderly population.

Tuning-free sparse clustering via alternating hard-thresholding

Model-based clustering is a commonly-used technique to partition heterogeneous data into homogeneous groups. When the analysis is to be conducted with a large number of features, analysts face simultaneous challenges in model interpretability, clustering accuracy, and computational efficiency. Several Bayesian and penalization methods have been proposed to select important features for model-based clustering. However, the performance of those methods relies on a careful algorithmic tuning, which can be time-consuming for high-dimensional cases. In this paper, we propose a new sparse clustering method based on alternating hard-thresholding. The new method is conceptually simple and tuning-free. With a user-specified sparsity level, it efficiently detects a set of key features by eliminating a large number of features that are less useful for clustering. Based on the selected key features, one can readily obtain an effective clustering of the original high-dimensional data under a general sparse covariance structure. Under mild conditions, we show that the new method leads to clusters with a misclassification rate consistent to the optimal rate as if the underlying true model were used. The promising performance of the new method is supported by both simulated and real data examples.

Temperature Dependency of Insect's Wingbeat Frequencies: An Empirical Approach to Temperature Correction.

This study examines the relationship between the wingbeat frequency of flying insects and ambient temperature, leveraging data from over 302,000 insect observations obtained using a near-infrared optical sensor during an eight-month field experiment. By measuring the wingbeat frequency as well as wing and body optical cross-sections of each insect in conjunction with the ambient temperature, we identified five clusters of insects and analyzed how their average wingbeat frequencies evolved over temperatures ranging from 10 °C to 38 °C. Our findings reveal a positive correlation between temperature and wingbeat frequency, with a more pronounced increase observed at higher wingbeat frequencies. Frequencies increased on average by 2.02 Hz/°C at 50 Hz, and up to 9.63 Hz/°C at 525 Hz, and a general model is proposed. This model offers a valuable tool for correcting wingbeat frequencies with temperature, enhancing the accuracy of insect clustering by optical and acoustic sensors. While this approach does not account for species-specific responses to temperature changes, our research provides a general insight, based on all species present during the field experiment, into the intricate dynamics of insect flight behavior in relation to environmental factors.

Application of non parametric Bayesian methods in high dimensional data

With the development of technology and the widespread collection of data, high-dimensional data analysis has become a research hotspot in many fields. Traditional parameter methods often face problems such as dimensional disasters in high-dimensional data analysis. Non parametric methods have broad application prospects in high-dimensional data because they do not rely on specific parameter distribution assumptions. The Bayesian rule is more suitable for dealing with noise and outliers in high-dimensional data because it takes uncertainty into account. Therefore, it is of great significance to combine non parametric methods with Bayesian methods for application research in high-dimensional data analysis. In this paper, the nonparametric Bayesian method was applied to the analysis of high-dimensional data, and the Dirichlet process Mixture model was used to cluster high-dimensional data. The regression analysis of high-dimensional data was carried out through the prediction model of nonparametric Bayesian regression. In this paper, the nonparametric Bayesian method based on Bayesian sparse linear model was used for feature selection of high-dimensional data. In order to determine the superiority of nonparametric Bayesian methods in high-dimensional data analysis, this paper conducted experiments on nonparametric Bayesian methods and traditional parametric methods in high-dimensional data analysis from five aspects of cluster analysis, classification analysis, regression analysis, feature selection and anomaly detection, and evaluated them through multiple indicators. This article explored the application of non parametric Bayesian methods in high-dimensional data analysis from these aspects through simulation experiments. The experimental results show that the clustering accuracy of the non parametric Bayesian clustering algorithm was 0.93, and the accuracy of the non parametric Bayesian classification algorithm was between 0.93 and 0.99; the coefficient of determination of nonparametric Bayesian regression algorithm was 0.98; the F1 values of non parametric Bayesian methods in anomaly detection ranged from 0.86 to 0.91, which was superior to traditional methods. Non parametric Bayesian methods have broad application prospects in high-dimensional data analysis, and can be applied in multiple fields such as clustering, classification, regression, etc.

Robust deep image clustering using convolutional autoencoder with separable discrete Krawtchouk and Hahn orthogonal moments

By cooperatively learning features and assigning clusters, deep clustering is superior to conventional clustering algorithms. Numerous deep clustering algorithms have been developed for a variety of application levels; however, the majority are still incapable of learning robust noise-resistant latent features, which limits the clustering performance. To address this open research challenge, we introduce, for the first time, a new approach called: Robust Deep Embedded Image Clustering algorithm with Separable Krawtchouk and Hahn Moments (RDEICSKHM). Our approach leverages the advantages of Krawtchouk and Hahn moments, such as local feature extraction, discrete orthogonality, and noise tolerance, to obtain a meaningful and robust image representation. Moreover, we employ LayerNormalization to further improve the latent space quality and facilitate the clustering process. We evaluate our approach on four image datasets: MNIST, MNIST-test, USPS, and Fashion-MNIST. We compare our method with several deep clustering methods based on two metrics: clustering accuracy (ACC) and normalized mutual information (NMI). The experimental results show that our method achieves superior or competitive performance on all datasets, demonstrating its effectiveness and robustness for deep image clustering.

Analysis of Parenting Stress, Co-parenting Level, Family Intimacy and Adaptability on Children's Social Skills via self-attention Network

This paper uses the self-attention method to track family intimacy. We propose the Self-Attention Adversarial Deep Subspace Clustering Algorithm (SAADSC). The self-attention adversarial network is used to impose a prior distribution constraint in the feature learning of the autoencoder, which guides the learned feature representation to be more robust, thereby improving the clustering accuracy. The results show that the proposed algorithm outperforms the state-of-the-art methods in terms of accuracy (ACC) and standard mutual information (NMI). Therefore, we can effectively transfer this self-attention method to the analysis of the effects of adaptation on children's social skills.

A Clustering Method for Single-Cell RNA-Seq Data Based on Automatic Weighting Penalty and Low-Rank Representation.

Advances in high-throughput single-cell RNA sequencing (scRNA-seq) technology have provided more comprehensive biological information on cell expression. Clustering analysis is a critical step in scRNA-seq research and provides clear knowledge of the cell identity. Unfortunately, the characteristics of scRNA-seq data and the limitations of existing technologies make clustering encounter a considerable challenge. Meanwhile, some existing methods treat different features equally and ignore differences in feature contributions, which leads to a loss of information. To overcome limitations, we introduce a weighted distance constraint into the construction of the similarity graph and combine the similarity constraint. We propose the Joint Automatic Weighting Similarity Graph and Low-rank Representation (JAGLRR) clustering method. Evaluating the contributions of each feature and assigning various weight values can increase the significance of valuable features while decreasing the interference of redundant features. The similarity constraint allows the model to generate a more symmetric affinity matrix. Benefitting from that affinity matrix, JAGLRR recovers the original linear relationship of the data more accurately and obtains more discriminative information. The results on simulated datasets and 8 real datasets show that JAGLRR outperforms 11 existing comparison methods in clustering experiments, with higher clustering accuracy and stability.

ScTPC: a novel semisupervised deep clustering model for scRNA-seq data.

Continuous advancements in single-cell RNA sequencing (scRNA-seq) technology have enabled researchers to further explore the study of cell heterogeneity, trajectory inference, identification of rare cell types, and neurology. Accurate scRNA-seq data clustering is crucial in single-cell sequencing data analysis. However, the high dimensionality, sparsity, and presence of "false" zero values in the data can pose challenges to clustering. Furthermore, current unsupervised clustering algorithms have not effectively leveraged prior biological knowledge, making cell clustering even more challenging. This study investigates a semisupervised clustering model called scTPC, which integrates the triplet constraint, pairwise constraint, and cross-entropy constraint based on deep learning. Specifically, the model begins by pretraining a denoising autoencoder based on a zero-inflated negative binomial distribution. Deep clustering is then performed in the learned latent feature space using triplet constraints and pairwise constraints generated from partial labeled cells. Finally, to address imbalanced cell-type datasets, a weighted cross-entropy loss is introduced to optimize the model. A series of experimental results on 10 real scRNA-seq datasets and five simulated datasets demonstrate that scTPC achieves accurate clustering with a well-designed framework. scTPC is a Python-based algorithm, and the code is available from https://github.com/LF-Yang/Code or https://zenodo.org/records/10951780.

Integration and Optimization of Educational Teaching Resources in Colleges and Universities Based on Clustering Algorithm

The paper presents Centroid Ranking Optimized Clustering (CROC), a novel approach aimed at enhancing educational teaching and resource optimization through advanced clustering techniques. CROC integrates centroid-based clustering with ranking optimization strategies to segment students or educational resources into meaningful clusters, facilitating personalized learning experiences and targeted interventions. Experimental analysis conducted on real-world educational datasets demonstrates that CROC outperforms traditional clustering algorithms in terms of clustering quality, accuracy, and interpretability. The findings suggest that CROC holds significant promise for informing data-driven decision-making in educational settings, enabling educators to better understand student performance patterns, identify at-risk students, and tailor instructional strategies to meet diverse learning needs. Experimental analysis on real-world educational datasets reveals that CROC achieves a Silhouette Score of 0.75 and a Davies-Bouldin Index of 0.40 on the MathScores dataset, outperforming traditional algorithms such as k-means (Silhouette Score: 0.68, Davies-Bouldin Index: 0.53), Hierarchical (Silhouette Score: 0.62, Davies-Bouldin Index: 0.57), and DBSCAN (Silhouette Score: 0.45, Davies-Bouldin Index: 0.78). Similarly, on the EnglishTest dataset, CROC attains a Silhouette Score of 0.82 and a Davies-Bouldin Index of 0.35, surpassing k-means (Silhouette Score: 0.74, Davies-Bouldin Index: 0.47), Hierarchical (Silhouette Score: 0.68, Davies-Bouldin Index: 0.52), and DBSCAN (Silhouette Score: 0.53, Davies-Bouldin Index: 0.68). These findings underscore the effectiveness of CROC in clustering educational data, enabling personalized learning experiences and data-driven decision-making in educational settings.

ScCompressSA: dual-channel self-attention based deep autoencoder model for single-cell clustering by compressing gene-gene interactions.

Single-cell clustering has played an important role in exploring the molecular mechanisms about cell differentiation and human diseases. Due to highly-stochastic transcriptomics data, accurate detection of cell types is still challenged, especially for RNA-sequencing data from human beings. In this case, deep neural networks have been increasingly employed to mine cell type specific patterns and haveoutperformed statistic approachesin cell clustering. Using cross-correlation to capture gene-gene interactions, this study proposes the scCompressSA method to integrate topological patterns from scRNA-seq data, with support of self-attention (SA) based coefficient compression (CC) block. This SA-based CC block is able to extract and employ static gene-gene interactions from scRNA-seq data. This proposed scCompressSA method has enhanced clustering accuracy in multiple benchmark scRNA-seq datasets by integrating topological and temporalfeatures. Static gene-gene interactions have been extracted as temporal features to boost clustering performance insingle-cell clustering For the scCompressSA method, dual-channel SA based CC block is able to integrate topological features and has exhibited extraordinary detection accuracy compared with previous clustering approaches that onlyemploy temporal patterns.

Fast Fusion Clustering via Double Random Projection.

In unsupervised learning, clustering is a common starting point for data processing. The convex or concave fusion clustering method is a novel approach that is more stable and accurate than traditional methods such as k-means and hierarchical clustering. However, the optimization algorithm used with this method can be slowed down significantly by the complexity of the fusion penalty, which increases the computational burden. This paper introduces a random projection ADMM algorithm based on the Bernoulli distribution and develops a double random projection ADMM method for high-dimensional fusion clustering. These new approaches significantly outperform the classical ADMM algorithm due to their ability to significantly increase computational speed by reducing complexity and improving clustering accuracy by using multiple random projections under a new evaluation criterion. We also demonstrate the convergence of our new algorithm and test its performance on both simulated and real data examples.

A Survey of Multiple Kernel Graph Clustering Algorithms

In recent years, algorithms based on multi-kernel learning and graph clustering have garnered significant attention in the field of data mining. This article reviews four advanced multi-kernel graph clustering algorithms: nearest neighbor linear kernel weighted multi-kernel graph clustering, multi-kernel graph clustering based on simultaneous global and local structure preservation, multi-kernel graph clustering based on direct consensus relationship graph learning, and custom non-negative matrix factorization multi-kernel graph tensor clustering. These algorithms have their own characteristics and, by combining multi-kernel learning, graph clustering, and other advanced technologies, they improve the accuracy and efficiency of clustering. However, they also face challenges such as high computational complexity and parameter settings, necessitating further research and optimization. These algorithms have wide application prospects in fields such as image recognition, text classification, and bioinformatics, providing new solutions for handling complex data structures.