Graph-based Clustering Research Articles

Improved protein interaction models predict differences in complexes between human cell lines.

The interactions of proteins to form complexes play a crucial role in cell function. Data on protein-protein or pairwise interactions (PPI) typically come from a combination of sample separation and mass spectrometry. Since 2010, several extensive, high-throughput mass spectrometry-based experimental studies have dramatically expanded public repositories for PPI data and, by extension, our knowledge of protein complexes. Unfortunately, challenges of limited overlap between experiments, modality-oriented biases, and prohibitive costs of experimental reproducibility continue to limit coverage of the human protein assembly map, both underscoring the need for and spurring the development of relevant computational approaches. Here, we present a new method for predicting the strength of protein interactions. It addresses two important issues that have limited past PPI prediction approaches: incomplete feature sets and incomplete proteome coverage. For a given collection of protein pairs, we fused data from heterogeneous sources into a feature matrix and identified the minimal set of feature partitions for which a non-empty set of protein pairs had complete values. For each such feature partition, we trained a classifier to predict PPI probabilities. We then calculated an overall prediction for a given protein pair by weighting the probabilities from all models that applied to that pair. Our approach accurately identified known and highly probable PPI, far exceeding the performance of current approaches and providing more complete proteome coverage. We then used the predicted probabilities to assemble complexes using previously-described graph-based tools and clustering algorithms and again obtained improved results. Lastly, we used features for three human cell lines to predict PPI and complex scores and identified complexes predicted to differ between those cell lines.

Data-driven machine learning approaches for precise lithofacies identification in complex geological environments

ABSTRACT Reservoir characterization is a vital task within the oil and gas industry, with the identification of lithofacies in subsurface formations being a fundamental aspect of this process. However, lithofacies identification in complex geological environments with high dimensions, such as the Lower Indus Basin in Pakistan, poses a notable challenge, especially when dealing with limited data. To address this issue, we propose four common data-driven machine learning approaches: multi-resolution graph-based clustering (MRGC), artificial neural networks (ANN), K-nearest neighbors (KNN), and self-organizing map (SOM). We utilized these proposed approaches to assess their performance in scenarios with varying core sample availability, specifically evaluating their effectiveness in identifying lithofacies within the Lower Goru formation of the middle Indus Basin. The study reveals that in scenarios with a limited number of core samples, MRGC is the preferred choice, while KNN or MRGC is more suitable for larger datasets. The results demonstrate the superior performance of MRGC and KNN in lithofacies identification within the specified geological environment, with SOM following closely behind, and ANN exhibiting comparatively lower efficacy. The accurate identification of lithofacies from the selected model is complemented by the application of the truncated Gaussian simulation method for facies modeling. Comparative results confirm the excellent agreement between the model identification of lithofacies from well logs and electro-facies obtained from the truncated Gaussian simulation electro-facies volume. This study highlights the crucial role of selecting the right machine learning approach for precise lithofacies identification and modeling in complex geological environments. The comparative analysis provides practitioners in the petroleum industry with insights into the strengths and limitations of each method, enhancing existing knowledge. In conclusion, this research emphasizes the significance of comprehensive research and method selection for advancing lithofacies identification in diverse formations or study areas, ultimately benefiting the broader field of subsurface characterization in the petroleum industry.

An End-to-End Approach for Graph-Based Multi-View Data Clustering

An End-to-End Approach for Graph-Based Multi-View Data Clustering

Multiview Tensor Spectral Clustering via Co-Regularization.

Graph-based multi-view clustering encodes multi-view data into sample affinities to find consensus representation, effectively overcoming heterogeneity across different views. However, traditional affinity measures tend to collapse as the feature dimension expands, posing challenges in estimating a unified alignment that reveals both cross-view and inner relationships. To tackle this challenge, we propose to achieve multi-view uniform clustering via consensus representation co-regularization. First, the sample affinities are encoded by both popular dyadic affinity and recent high-order affinities to comprehensively characterize spatial distributions of the HDLSS data. Second, a fused consensus representation is learned through aligning the multi-view low-dimensional representation by co-regularization. The learning of the fused representation is modeled by a high-order eigenvalue problem within manifold space to preserve the intrinsic connections and complementary correlations of original data. A numerical scheme via manifold minimization is designed to solve the high-order eigenvalue problem efficaciously. Experiments on eight HDLSS datasets demonstrate the effectiveness of our proposed method in comparison with the recent thirteen benchmark methods.

Multi-View and Multi-Order Structured Graph Learning.

Recently, graph-based multi-view clustering (GMC) has attracted extensive attention from researchers, in which multi-view clustering based on structured graph learning (SGL) can be considered as one of the most interesting branches, achieving promising performance. However, most of the existing SGL methods suffer from sparse graphs lacking useful information, which normally appears in practice. To alleviate this problem, we propose a novel multi-view and multi-order SGL ( [Formula: see text]SGL) model which introduces multiple different orders (multi-order) graphs into the SGL procedure reasonably. To be more specific, [Formula: see text]SGL designs a two-layer weighted-learning mechanism, in which the first layer truncatedly selects part of views in different orders to retain the most useful information, and the second layer assigns smooth weights into retained multi-order graphs to fuse them attentively. Moreover, an iterative optimization algorithm is derived to solve the optimization problem involved in [Formula: see text]SGL, and the corresponding theoretical analyses are provided. In experiments, extensive empirical results demonstrate that the proposed [Formula: see text]SGL model achieves the state-of-the-art performance in several benchmarks.

Pore Size Classification and Prediction Based on Distribution of Reservoir Fluid Volumes Utilizing Well Logs and Deep Learning Algorithm in a Complex Lithology

Pore size analysis plays a pivotal role in unraveling reservoir behavior and its intricate relationship with confined fluids. Traditional methods for predicting pore size distribution (PSD), relying on drilling cores or thin sections, face limitations associated with depth specificity. In this study, we introduce an innovative framework that leverages nuclear magnetic resonance (NMR) log data, encompassing clay-bound water (CBW), bound volume irreducible (BVI), and free fluid volume (FFV), to determine three PSDs (micropores, mesopores, and macropores). Moreover, we establish a robust pore size classification (PSC) system utilizing ternary plots, derived from the PSDs.Within the three studied wells, NMR log data is exclusive to one well (well-A), while conventional well logs are accessible for all three wells (well-A, well-B, and well-C). This distinction enables PSD predictions for the remaining two wells (B and C). To prognosticate NMR outputs (CBW, BVI, FFV) for these wells, a two-step deep learning (DL) algorithm is implemented. Initially, three feature selection algorithms (f-classif, f-regression, and mutual-info-regression) identify the conventional well logs most correlated to NMR outputs in well-A. The three feature selection algorithms utilize statistical computations. These algorithms are utilized to systematically identify and optimize pertinent input features, thereby augmenting model interpretability and predictive efficacy within intricate data-driven endeavors. So, all three feature selection algorithms introduced the number of 4 logs as the most optimal number of inputs to the DL algorithm with different combinations of logs for each of the three desired outputs. Subsequently, the CUDA Deep Neural Network Long Short-Term Memory algorithm(CUDNNLSTM), belonging to the category of DL algorithms and harnessing the computational power of GPUs, is employed for the prediction of CBW, BVI, and FFV logs. This prediction leverages the optimal logs identified in the preceding step. Estimation of NMR outputs was done first in well-A (80% of data as training and 20% as testing). The correlation coefficient (CC) between the actual and estimated data for the three outputs CBW, BVI and FFV are 95%, 94%, and 97%, respectively, as well as root mean square error (RMSE) was obtained 0.0081, 0.098, and 0.0089, respectively. To assess the effectiveness of the proposed algorithm, we compared it with two traditional methods for log estimation: multiple regression and multi-resolution graph-based clustering methods. The results demonstrate the superior accuracy of our algorithm in comparison to these conventional approaches. This DL-driven approach facilitates PSD prediction grounded in fluid saturation for wells B and C.Ternary plots are then employed for PSCs. Seven distinct PSCs within well-A employing actual NMR logs (CBW, BVI, FFV), in conjunction with an equivalent count within wells B and C utilizing three predicted logs, are harmoniously categorized leading to the identification of seven distinct pore size classification facies (PSCF). this research introduces an advanced approach to pore size classification and prediction, fusing NMR logs with deep learning techniques and extending their application to nearby wells without NMR log. The resulting PSCFs offer valuable insights into generating precise and detailed reservoir 3D models.

A framework for spatial-temporal cluster evolution representation and analysis based on graphs

Analysis on spatial-temporal data has several benefits that range from an improved traffic network in a city to increased earnings for drivers and ridesharing companies. A common analysis technique is clustering. However, most clustering techniques consider a constrained representation of clusters that is limited to a single timestamp. Evolving clusters exist through several timestamps and interact with other spatial-temporal objects or clusters, having cluster relationships. When many evolving clusters exist for analysis, a graph can be used to represent cluster evolution. In this article, we propose a framework for graph-based cluster evolution representation and analysis. The framework represents cluster structure and relationships as well as provides a graph representation of cluster evolution for analysis. Evaluation is done in three case studies with spatial-temporal data about taxis that can identify important phenomena or trends in movements in a city for traffic improvement.

Community detection using Jaya optimization algorithm based on deep learning methods and KNN graph-based clustering

Purpose In this paper, community identification has been considered as the most critical task of social network analysis. The purpose of this paper is to organize the nodes of a given network graph into distinct clusters or known communities. These clusters will therefore form the different communities available within the social network graph. Design/methodology/approach To date, numerous methods have been developed to detect communities in social networks through graph clustering techniques. The k-means algorithm stands out as one of the most well-known graph clustering algorithms, celebrated for its straightforward implementation and rapid processing. However, it has a serious drawback because it is insensitive to initial conditions and always settles on local optima rather than finding the global optimum. More recently, clustering algorithms that use a reciprocal KNN (k-nearest neighbors) graph have been used for data clustering. It skillfully overcomes many major shortcomings of k-means algorithms, especially about the selection of the initial centers of clusters. However, it does face its own challenge: sensitivity to the choice of the neighborhood size parameter k, which is crucial for selecting the nearest neighbors during the clustering process. In this design, the Jaya optimization method is used to select the K parameter in the KNN method. Findings The experiment on real-world network data results show that the proposed approach significantly improves the accuracy of methods in community detection in social networks. On the other hand, it seems to offer some potential for discovering a more refined hierarchy in social networks and thus becomes a useful tool in the analysis of social networks. Originality/value This paper introduces an enhancement to the KNN graph-based clustering method by proposing a local average vector method for selecting the optimal neighborhood size parameter k. Furthermore, it presents an improved Jaya algorithm with KNN graph-based clustering for more effective community detection in social network graphs.

Real-time detection of spoken speech from unlabeled ECoG signals: A pilot study with an ALS participant.

Brain-Computer Interfaces (BCIs) hold significant promise for restoring communication in individuals with partial or complete loss of the ability to speak due to paralysis from amyotrophic lateral sclerosis (ALS), brainstem stroke, and other neurological disorders. Many of the approaches to speech decoding reported in the BCI literature have required time-aligned target representations to allow successful training - a major challenge when translating such approaches to people who have already lost their voice. In this pilot study, we made a first step toward scenarios in which no ground truth is available. We utilized a graph-based clustering approach to identify temporal segments of speech production from electrocorticographic (ECoG) signals alone. We then used the estimated speech segments to train a voice activity detection (VAD) model using only ECoG signals. We evaluated our approach using held-out open-loop recordings of a single dysarthric clinical trial participant living with ALS, and we compared the resulting performance to previous solutions trained with ground truth acoustic voice recordings. Our approach achieves a median error rate of around 0.5 seconds with respect to the actual spoken speech. Embedded into a real-time BCI, our approach is capable of providing VAD results with a latency of only 10 ms. To the best of our knowledge, our results show for the first time that speech activity can be predicted purely from unlabeled ECoG signals, a crucial step toward individuals who cannot provide this information anymore due to their neurological condition, such as patients with locked-in syndrome. ClinicalTrials.gov, registration number NCT03567213.

Joint consensus kernel learning and adaptive hypergraph regularization for graph-based clustering

Recent advancements in multiple kernel learning-based graph clustering methods have demonstrated significant promise in effectively learning a consensus kernel matrix from various candidate kernel matrices. This approach enables the creation of a low-dimensional representation in the kernel space of high-dimensional data through self-expressiveness. However, a key challenge remains in capturing the latent geometric properties embedded within different kernel matrices to enhance data representation. In this paper, we propose a novel method called joint consensus kernel learning and adaptive hypergraph regularization for graph-based clustering (JKHR). Our approach integrates an innovative adaptive hypergraph Laplacian regularizer, which is characterized by the fusion of multiple nearest neighbor kernel graphs, into the multiple kernel learning-based graph clustering framework. JKHR jointly and adaptively optimizes both the consensus kernel matrix and the hypergraph Laplacian regularizer to achieve a low-dimensional representation that effectively preserves the intrinsic geometry of the data. Experimental results on both synthetic and real benchmark datasets demonstrate that JKHR outperforms state-of-the-art self-expressiveness-based graph clustering methods as well as traditional clustering techniques.

Sequential Activation of Lateral Hypothalamic Neuronal Populations during Feeding and Their Assembly by Gamma Oscillations.

Neural circuits supporting innate behaviors, such as feeding, exploration, and social interaction, intermingle in the lateral hypothalamus (LH). Although previous studies have shown that individual LH neurons change their firing relative to the baseline during one or more behaviors, the firing rate dynamics of LH populations within behavioral episodes and the coordination of behavior-related LH populations remain largely unknown. Here, using unsupervised graph-based clustering of LH neurons firing rate dynamics in freely behaving male mice, we identified distinct populations of cells whose activity corresponds to feeding, specific times during feeding bouts, or other innate behaviors-social interaction and novel object exploration. Feeding-related cells fired together with a higher probability during slow and fast gamma oscillations (30-60 and 60-90 Hz) than during nonrhythmic epochs. In contrast, the cofiring of neurons signaling other behaviors than feeding was overall similar between slow gamma and nonrhythmic epochs but increased during fast gamma oscillations. These results reveal a neural organization of ethological hierarchies in the LH and point to behavior-specific motivational systems, the dysfunction of which may contribute to mental disorders.

Multi-view clustering via double spaces structure learning and adaptive multiple projection regression learning

Multi-view clustering aims to group objects with high similarity into one group according to the heterogeneous features of different views. The graph-based clustering methods have obtained excellent results. However, there remain a few common drawbacks. For example, some methods do not consider graphs' high-order structure information. Thus, fuller data information cannot be obtained. In addition, some methods remove noise, outliers, and redundant information in the graph learning phase, resulting in the loss of graph information. Furthermore, using predefined graphs cannot exploit complementary information between views. A triple strategy-based multi-view clustering method is presented to solve the above issues. First, Laplacian graphs are used for fusion learning, and the underlying first-order and second-order structure information among views are explored simultaneously. Then, a label fusion scheme is designed to eliminate noise, outliers, and redundant information and to mine the intrinsic characteristics of data labels. Besides, the consistent label matrix in adaptive regression learning is used to explore complementary information between views in a mutually guided learning way. Finally, the objective function is solved by using an efficient iterative method. Six types of experiments are conducted on eleven real-world multi-view datasets, and the conclusions that can be drawn are: (1) the proposed algorithm achieves the best results in terms of clustering accuracy on ten datasets with an average accuracy improvement of 5.11% compared to other algorithms. Specifically, the accuracy improved by 9.05% on dataset HW and 10.95% on dataset Reuters compared to the second results; (2) The ablation experiments confirm that the different learning strategies included in the proposed algorithm allow it to achieve better clustering performance.

Co-regularized optimal high-order graph embedding for multi-view clustering

Real-world applications frequently involve multiple data modalities in the same samples, which are regarded as multi-view data. Multi-view clustering has been studied extensively in recent years to demonstrate embedded heterogeneity. However, most existing methods emphasize low-order correlation in multiple views, whereas approaches that incorporate high-order correlation are limited by the equal view-specific significance problem or a trade-off between global and local consistency. In this paper, we propose a co-regularized optimal graph-based clustering method known as Co-MSE, which integrates the correlation of different orders. By integrating the first-order and second-order similarities, the local structure is preserved, while an optimized embedding representation for multi-view data is obtained simultaneously through co-regularization. We demonstrate that Co-MSE can aid in providing a more suitable embedding representation and further enable satisfactory clustering performance. Extensive experiments on real-world datasets confirm the effectiveness and advantages of the proposed method.

Determining the geomechanical units using rock physics methods

The stability of the drilled wellbores in carbonate formations is of great importance. This paper is a new approach to determine the elastic properties of the wellbore based on rock physics methods using the Hashin-Shtrikman bounds to determine the wellbore stability. Initially, the static elastic parameters were determined by two different methods, namely the rock physics method and DSI log, followed by clustering through Multi-Resolutional Graph-Based clustering (MRGC) and calculating geomechanical-units (GMUs) of each method separately. The obtained results from DSI log and rock physic methods were then compared followed by determining the wellbore stability. The results showed that the correlation between shear and compressional wave velocity obtained from the petrophysical method with the measured values of shear and pressure wave velocity in the well was 0.94 and 0.90, respectively, which shows that the petrophysical method can estimate these two logs with high accuracy, and it can be a suitable alternative instead of using the Dipole Shear Sonic Imager (DSI). It was observed that the geomechanical units of elastic parameters calculated by proposed rock physics method are in good agreement with those obtained from DSI log. Rock-physic method can be a good alternative for when expensive DSI logs are missing.

Multi-view clustering via high-order bipartite graph fusion

Multi-view clustering is widely applied in engineering and scientific research. It helps reveal the underlying structures and correlations behind complex multi-view data. Graph-based multi-view clustering stands as a prominent research frontier within the multi-view clustering field, yet faces persistent challenges. Firstly, typically constructed initial input graphs for each view yields sparse clustering structure, hindering clustering performance. Secondly, as data sources proliferate, algorithms encounter escalating time complexities, notably in methods relying on n×n fully connected graphs. Thirdly, prevailing graph fusion strategies struggle to mitigate the impact of low-quality graphs, impeding overall efficacy. In this paper, we present a novel Multi-View Clustering method based on High-Order Bipartite Graph fusion (MCHBG). For the first two challenges, the introduced high-order bipartite graphs in MCHBG reveal richer clustering structures, effectively alleviating sparse clustering structure of the input graph, while keeping the overall algorithm’s computational complexity controlled within O(n). For the third challenge, our graph fusion mechanism selectively integrates high-order bipartite graphs, and implicitly weights the selected bipartite graphs to mitigate the impact of low-quality bipartite graphs. MCHBG learns a structured fusion bipartite graph under the Laplacian rank constraint, which directly indicates the clusters of data. Extensive experimental results demonstrate the effectiveness and superiority of MCHBG. Code available: https://anonymous.4open.science/r/MCHBG.

14 Natural killer cells have impaired cytotoxicity in advanced renal cell carcinoma

Abstract Background Natural killer (NK) cells are key mediators of anti-tumor activity in multiple cancers, including clear cell renal cell carcinoma (ccRCC). However, the phenotype and function of NK cells across different disease stages of ccRCC is incompletely characterized. Methods Single-cell RNA (scRNA)-sequencing (10x Genomics) data from tumor and adjacent normal kidney from ccRCC was analyzed at multiple clinical stages (localized – stage I/II/III; and advanced – stage IV). Graph-based clustering and lineage markers were used to identify distinct NK cell populations. Differential gene expression analysis was performed to further investigate NK cell phenotype and to derive a gene expression signature (GES). Gene signatures from NK cell subclusters of interest were used to interrogate bulk transcriptomic datasets and expression with clinical outcomes. Tumor-infiltrating NK cell function (cytokine production and cytotoxicity) was assessed by isolation of live NK cells from ccRCC tissue, co-culture with K562 target cells, and measurement of cytokine (IFNgamma) and cytotoxicity (CD107a) markers by flow cytometry. Results Single-cell transcriptomic data were analyzed from 13 patients with ccRCC (tumor and normal kidney), resulting in 21,139 high-quality NK cells. Patients with advanced/metastatic RCC had a lower proportion of NK cells versus total immune cells in the tumor microenvironment compared to normal kidney (p=0.036) and localized ccRCC (p=0.088). Clustering analysis revealed 6 distinct NK cell subsets. The C0.Bright-like NK cell cluster was significantly enriched in advanced ccRCC compared to localized ccRCC (p=0.048) and normal kidney (p=0.0059), expressed markers of tissue residency (ZNF683, ITGA1, CD9, ITGAE), and had decreased expression of cytotoxicity genes (GZMB, PRF1). A GES signature composed of genes upregulated in the dysfunctional C0.Bright-like NK cell cluster was used to interrogate bulk RNA-sequencing data from The Cancer Genome Atlas clear cell cohort, providing validation in a large, independent cohort, of an increase in this dysfunctional NK subset in advanced ccRCC compared to localized ccRCC (p=0.00039) and normal tissue (p=5.3e-05). To functionally confirm the decreased cytotoxicity of this dysfunctional NK population, CD45+CD56+CD3- NK cells were isolated from advanced ccRCC, and co-cultured with K562 target cells. Consistent with the transcriptomic phenotype, tumor-resident CD49a+CD9+ NK cells had cytokine production (IFNgamma) but decreased markers of cytotoxicity (CD107a) compared to CD49a-CD9- NK cells. By contrast, CD49a+CD9+ NK cells isolated from peripheral blood or normal kidney tissue maintained cytotoxic activity. Conclusions Among patients with ccRCC, a single-cell transcriptomic analysis revealed heterogeneous NK cell populations. A dysfunctional tumor resident NK cell phenotype was enriched among patients with advanced disease in ccRCC, and functionally confirmed to have diminished cytotoxicity. This study therefore provides insight into a new axis of immune dysfunction – decreased NK-mediated cytotoxicity – in advanced ccRCC. An improved understanding of NK cell dysfunction within ccRCC may provide a foundation for therapeutic restoration of NK-mediated anti-tumor activity in ccRCC. DOD CDMRP Funding: yes

Efficient Multi-View Clustering via Unified and Discrete Bipartite Graph Learning.

Although previous graph-based multi-view clustering (MVC) algorithms have gained significant progress, most of them are still faced with three limitations. First, they often suffer from high computational complexity, which restricts their applications in large-scale scenarios. Second, they usually perform graph learning either at the single-view level or at the view-consensus level, but often neglect the possibility of the joint learning of single-view and consensus graphs. Third, many of them rely on the k -means for discretization of the spectral embeddings, which lack the ability to directly learn the graph with discrete cluster structure. In light of this, this article presents an efficient MVC approach via u nified and d iscrete b ipartite g raph l earning (UDBGL). Specifically, the anchor-based subspace learning is incorporated to learn the view-specific bipartite graphs from multiple views, upon which the bipartite graph fusion is leveraged to learn a view-consensus bipartite graph with adaptive weight learning. Furthermore, the Laplacian rank constraint is imposed to ensure that the fused bipartite graph has discrete cluster structures (with a specific number of connected components). By simultaneously formulating the view-specific bipartite graph learning, the view-consensus bipartite graph learning, and the discrete cluster structure learning into a unified objective function, an efficient minimization algorithm is then designed to tackle this optimization problem and directly achieve a discrete clustering solution without requiring additional partitioning, which notably has linear time complexity in data size. Experiments on a variety of multi-view datasets demonstrate the robustness and efficiency of our UDBGL approach. The code is available at https://github.com/huangdonghere/UDBGL.

Typicality-Aware Adaptive Similarity Matrix for Unsupervised Learning.

Graph-based clustering approaches, especially the family of spectral clustering, have been widely used in machine learning areas. The alternatives usually engage a similarity matrix that is constructed in advance or learned from a probabilistic perspective. However, unreasonable similarity matrix construction inevitably leads to performance degradation, and the sum-to-one probability constraints may make the approaches sensitive to noisy scenarios. To address these issues, the notion of typicality-aware adaptive similarity matrix learning is presented in this study. The typicality (possibility) rather than the probability of each sample being a neighbor of other samples is measured and adaptively learned. By introducing a robust balance term, the similarity between any pairs of samples is only related to the distance between them, yet it is not affected by other samples. Therefore, the impact caused by the noisy data or outliers can be alleviated, and meanwhile, the neighborhood structures can be well captured according to the joint distance between samples and their spectral embeddings. Moreover, the generated similarity matrix has block diagonal properties that are beneficial to correct clustering. Interestingly, the results optimized by the typicality-aware adaptive similarity matrix learning share the common essence with the Gaussian kernel function, and the latter can be directly derived from the former. Extensive experiments on synthetic and well-known benchmark datasets demonstrate the superiority of the proposed idea when comparing with some state-of-the-art methods.

CABGSI: An efficient clustering algorithm based on structural information of graphs

This paper introduces CABGSI, a novel graph-based clustering algorithm that effectively addresses the limitations of traditional clustering techniques. Unlike conventional methods that require predefined cluster quantities and assume simple geometrical data structures, CABGSI leverages graph structural entropy to naturally discern clusters. Our algorithm constructs a network among data points, capturing the intrinsic structure of the data. We demonstrate its superior performance on diverse datasets, particularly those with complex geometries such as speckles and images. Despite challenges with moon and ring configurations, CABGSI significantly outperforms traditional algorithms like k-means by not relying on predetermined cluster centers. This innovation broadens the applicability of clustering techniques and paves the way for future research in data analysis.

One-step graph-based multi-view clustering via specific and unified nonnegative embeddings

One-step graph-based multi-view clustering via specific and unified nonnegative embeddings