Reliable Clustering Research Articles

Membership determination in open clusters using the DBSCAN Clustering Algorithm

In this paper, we apply the machine learning clustering algorithm Density Based Spatial Clustering of Applications with Noise (DBSCAN) to study the membership of stars in twelve open clusters (NGC 2264, NGC 2682, NGC 2244, NGC 3293, NGC 6913, NGC 7142, IC 1805, NGC 6231, NGC 2243, NGC 6451, NGC 6005 and NGC 6583) based on Gaia DR3 Data. This sample of clusters spans a variety of parameters like age, metallicity, distance, extinction and a wide parameter space in proper motions and parallaxes. We obtain reliable cluster members using DBSCAN as faint as G∼20 mag and also in the outer regions of clusters. With our revised membership list, we plot color-magnitude diagrams and we obtain cluster parameters for our sample using ASteCA and compare it with the catalog values. We also validate our membership sample by spectroscopic data from APOGEE and GALAH for the available data. This paper demonstrates the effectiveness of DBSCAN in membership determination of clusters.

JWST NIRCam Photometry: A Study of Globular Clusters Surrounding Bright Elliptical Galaxy VV 191a at z = 0.0513

James Webb Space Telescope NIRCam images have revealed 154 reliable globular cluster (GC) candidates around the z = 0.0513 elliptical galaxy VV 191a after subtracting 34 likely interlopers from background galaxies inside our search area. NIRCam broadband observations are made at 0.9–4.5 μm using the F090W, F150W, F356W, and F444W filters. Using point-spread-function-matched photometry, the data are analyzed to present color–magnitude diagrams and color distributions that suggest a relatively uniform population of GCs, except for small fractions of reddest (5%–8%) and bluest (2%–4%) outliers. GC models in the F090W versus (F090W–F150W) diagram fit the NIRCam data well and show that the majority of GCs detected have a mass of ∼106.5 M ⊙, with metallicities [Fe/H] spanning the typical range expected for GCs (−2.5 ≲ [Fe/H]≲ 0.5). However, the models predict ∼0.3–0.4 mag bluer (F356W–F444W) colors than the NIRCam data for a reasonable range of GC ages, metallicities, and reddening. Although our data do not quite reach the luminosity function turnover, the measured luminosity function is consistent with previous measurements, suggesting an estimated peak at m AB ∼ −9.4 ± 0.2 mag in the F090W filter.

Attribute-Missing Graph Clustering Network

Deep clustering with attribute-missing graphs, where only a subset of nodes possesses complete attributes while those of others are missing, is an important yet challenging topic in various practical applications. It has become a prevalent learning paradigm in existing studies to perform data imputation first and subsequently conduct clustering using the imputed information. However, these ``two-stage" methods disconnect the clustering and imputation processes, preventing the model from effectively learning clustering-friendly graph embedding. Furthermore, they are not tailored for clustering tasks, leading to inferior clustering results. To solve these issues, we propose a novel Attribute-Missing Graph Clustering (AMGC) method to alternately promote clustering and imputation in a unified framework, where we iteratively produce the clustering-enhanced nearest neighbor information to conduct the data imputation process and utilize the imputed information to implicitly refine the clustering distribution through model optimization. Specifically, in the imputation step, we take the learned clustering information as imputation prompts to help each attribute-missing sample gather highly correlated features within its clusters for data completion, such that the intra-class compactness can be improved. Moreover, to support reliable clustering, we maximize inter-class separability by conducting cost-efficient dual non-contrastive learning over the imputed latent features, which in turn promotes greater graph encoding capability for clustering sub-network. Extensive experiments on five datasets have verified the superiority of AMGC against competitors.

Longmixr: a tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data types.

Accurate clustering of mixed data, encompassing binary, categorical, and continuous variables, is vital for effective patient stratification in clinical questionnaire analysis. To address this need, we present longmixr, a comprehensive R package providing a robust framework for clustering mixed longitudinal data using finite mixture modeling techniques. By incorporating consensus clustering, longmixr ensures reliable and stable clustering results. Moreover, the package includes a detailed vignette that facilitates cluster exploration and visualization. The R package is freely available at https://cran.r-project.org/package=longmixr with detailed documentation, including a case vignette, at https://cellmapslab.github.io/longmixr/.

Energy-Efficient and Reliable Clustering with Optimized Scheduling and Routing for Wireless Sensor Networks

Energy-Efficient and Reliable Clustering with Optimized Scheduling and Routing for Wireless Sensor Networks

Sequential estimation for mixture of regression models for heterogeneous population

Heterogeneity among patients commonly exists in clinical studies and leads to challenges in medical research. It is widely accepted that there exist various sub-types in the population and they are distinct from each other. The approach of identifying the sub-types and thus tailoring disease prevention and treatment is known as precision medicine. The mixture model is a classical statistical model to cluster the heterogeneous population into homogeneous sub-populations. However, for the highly heterogeneous population with multiple components, its parameter estimation and clustering results may be ambiguous due to the dependence of the EM algorithm on the initial values. For sub-typing purposes, the finite mixture of regression models with concomitant variables is considered and a novel statistical method is proposed to identify the main components with large proportions in the mixture sequentially. Compared to existing typical statistical inferences, the new method not only requires no pre-specification on the number of components for model fitting, but also provides more reliable parameter estimation and clustering results. Simulation studies demonstrated the superiority of the proposed method. Real data analysis on the drug response prediction illustrated its reliability in the parameter estimation and capability to identify the important subgroup.

ScMLC: an accurate and robust multiplex community detection method for single-cell multi-omics data.

Clustering cells based on single-cell multi-modal sequencing technologies provides an unprecedented opportunity to create high-resolution cell atlas, reveal cellular critical states and study health and diseases. However, effectively integrating different sequencing data for cell clustering remains a challenging task. Motivated by the successful application of Louvain in scRNA-seq data, we propose a single-cell multi-modal Louvain clustering framework, called scMLC, to tackle this problem. scMLC builds multiplex single- and cross-modal cell-to-cell networks to capture modal-specific and consistent information between modalities and then adopts a robust multiplex community detection method to obtain the reliable cell clusters. In comparison with 15 state-of-the-art clustering methods on seven real datasets simultaneously measuring gene expression and chromatin accessibility, scMLC achieves better accuracy and stability in most datasets. Synthetic results also indicate that the cell-network-based integration strategy of multi-omics data is superior to other strategies in terms of generalization. Moreover, scMLC is flexible and can be extended to single-cell sequencing data with more than two modalities.

Clustering honey samples with unsupervised machine learning methods using FTIR data.

This study utilizes Fourier transform infrared (FTIR) data from honey samples to cluster and categorize them based on their spectral characteristics. The aim is to group similar samples together, revealing patterns and aiding in classification. The process begins by determining the number of clusters using the elbow method, resulting in five distinct clusters. Principal Component Analysis (PCA) is then applied to reduce the dataset's dimensionality by capturing its significant variances. Hierarchical Cluster Analysis (HCA) further refines the sample clusters. 20% of the data, representing identified clusters, is randomly selected for testing, while the remainder serves as training data for a deep learning algorithm employing a multilayer perceptron (MLP). Following training, the test data are evaluated, revealing an impressive 96.15% accuracy. Accuracy measures the machine learning model's ability to predict class labels for new data accurately. This approach offers reliable honey sample clustering without necessitating extensive preprocessing. Moreover, its swiftness and cost-effectiveness enhance its practicality. Ultimately, by leveraging FTIR spectral data, this method successfully identifies similarities among honey samples, enabling efficient categorization and demonstrating promise in the field of spectral analysis in food science.

Phytohabitans aurantiacus sp. nov., an actinomycete isolated from soil.

A novel actinomycete, designated RD004123T, was isolated from a soil sample collected in Hokkaido, Japan, and its taxonomic position was investigated by a polyphasic approach. Phylogenetic analysis based on 16S rRNA gene sequence comparisons revealed that strain RD004123T fell within the cluster of the family Micromonosporaceae but did not form a reliable cluster with any member of the family. The similarity values between strain RD004123T and the type species of 29 genera in the family Micromonosporaceae were 91.7-97.7 %. Meanwhile, phylogenomic analyses indicated that strain RD004123T was closely related to members of the genus Phytohabitans. Strain RD004123T contained both meso-diaminopimelic acid and l-lysine as the diagnostic diamino acids of the peptidoglycan. The predominant isoprenoid quinones were MK-10(H8) and MK-10(H6), and the major fatty acids were anteiso-C17 : 0, iso-C16 : 0, iso-C15 : 0 and C17 : 0. The detected polar lipids were phosphatidylinositol mannosides, phosphatidylinositol, phosphatidylethanolamine and diphosphatidylglycerol. These chemotaxonomic features corresponded to those of the genus Phytohabitans. Meanwhile, the results of genome comparison analyses and phenotypic characterizations distinguished strain RD004123T from the other members of the genus Phytohabitans. Therefore, strain RD004123T should be assigned as representing a novel species of the genus Phytohabitans, for which the name Phytohabitans aurantiacus sp. nov. is proposed. The type strain is RD004123T (=NBRC 114997T=DSM 114330T).

K-Means Hybridization with Enhanced Firefly Algorithm for High-Dimension Automatic Clustering

K-means clustering is not able to select the right number of clusters of data items having high-dimension. So, for determining the ideal number of clusters, we have combined PCA with the Silhouette and Elbow approaches. Additionally, we have a large number of meta-heuristic swarm intelligence algorithms which is influenced by nature and were previously used to solve the automatic data clustering problem. Firefly offers reliable and effective automatic data clustering. The Firefly algorithm automatically divides the entire population into subpopulations, which slows down the convergence and reduces the of capturing local minima in high-dimensional optimization problems. Thus, for automatic clustering, we demonstrated an improved firefly, i.e., we offered a hybridized K-means with an ODFA model. The experimental section displays the results and graphs for the Silhouette, Elbow, and Firefly algorithms.

A clustering (DBSCAN+GMM) investigation of the young open cluster NGC 6649

ABSTRACT We investigate the spatial structure and dynamical properties of the young (∼60 Myr), richly populated open cluster NGC 6649 based on reliable cluster members from Gaia-DR3 data. The density-based spatial clustering of applications with noise is used to estimate membership probabilities for 25045 sample stars in the field of the cluster. More than 1000 likely cluster members down to G ∼ 20 mag can be identified based on the membership probabilities. The cluster members of NGC 6649 extend up to a radial distance of ${\sim} 21\,\mathrm{ arcmin}$ (12 pc), which is much larger than previously thought. We estimate a core radius of $R_{\mathrm{ c}}=2\overset{^{\prime }}{.}38\pm 0\overset{^{\prime }}{.}06$ (1.4 pc) and a tidal radius of $R_{\mathrm{ t}}=35\overset{^{\prime }}{.}85\pm 6\overset{^{\prime }}{.}64$ (20.9 pc) for the cluster, indicating that the cluster has formed a dense core and an extended halo. We find that mass segregation has taken place within the cluster. We also detect two small and sparse clusters (C1 and C2) around NGC 6649. C1 has recently been reported, but C2 is detected for the first time. The Gaussian mixture model clustering method is used to identify 184 and 60 members for C1 and C2, respectively. We find that NGC 6649, C1, and C2 are located at similar distances. Moreover, we estimate a small spatial separation of ∼11.6 pc between C1 and NGC 6649, which is close to or slightly smaller than the detectable radius of NGC 6649. This indicates that NGC 6649 and C1 are physically connected, and there may exist strong tidal interactions and mass transfer between the two clusters. C2 shows a significant lack of faint members (G > 17 mag), probably due to star evaporation and tidal stripping. Our results show that NGC 6649 may be the dominant cluster in a triple-cluster system (NGC 6649+C1+C2).

Geometric-inspired graph-based Incomplete Multi-view Clustering

Multi-view clustering methods group data into different clusters by discovering the consensus in heterogeneous sources, which however becomes difficult when partial views of real-world data are missing. Consequently, reducing the impact of missing views and leveraging available views are the key concerns for the Incomplete Multi-view Clustering (IMvC) problem. In this research, we take an innovative, geometry-based perspective to investigate the IMvC problem under a commonly-used weight aggregation framework. We conduct a geometric analysis to understand how missing views shift the aggregation solution from the one achieved with full views, subsequently impacting the clustering result. Drawing from our analysis, we introduce a weight reallocation approach that minimizes the shift and approximates the full-view solution by reallocating the factual weight of each available view. Furthermore, we address the IMvC problem by using our reallocation method on a graph aggregation algorithm to obtain reliable clusters. Our extensive experiments demonstrate that our proposed approach outperforms previous IMvC methods, reporting superior results on four datasets with three metrics. Especially, on the Caltech101-7 dataset with 40 percent missing data, our method achieves an accuracy of 0.686, which significantly outperforms the results of other comparison methods that are no larger than 0.662. Further, our method can be used as a flexible plugin to improve other weight aggregation algorithms. The source code of this work is publicly available at https://github.com/bjlfzs/Geometric-Inspired-Graph-based-Incomplete-Multi-view-Clustering.

Reliable cluster based data collection framework for IoT-big data healthcare applications

Internet of Things (IoT) devices installed in hospital direct data unceasingly; in this manner, energy usage augments with the number of broadcasts too. In this paper, Reliable Cluster based Data Collection Framework (RCDCF) for IoT-Big Data Healthcare Applications (HA) is developed. During clustering process, the connected IoT devices are grouped into clusters. In clustering technique, the available IoT devices are gathered into groups. The device with high battery capacity and processing ability is selected as a cluster head (CH). Each member of the cluster is allocated multiple slots by applying a general function pooled by the Fog node and the entire devices. To perceive and eliminate outliers from the sensor data, Density-based spatial clustering of applications with noise (DBSCAN) method is utilized. To forecast the objective and subjective behaviours of the equipments, a Random Forest Deep Neural Network (RF-DNN) based classification model is utilized. By experimental results, it has been shown that RCDCF achieves 19% and 20% reduced energy consumption at Cloud and Fog centers, respectively. Moreover, RCDCF has 2.1% and 1.3% increased correctness of data at Cloud and Fog data centers, respectively, when compared to the existing framework.

Cloud migration framework clustering method for social decision support in modernizing the legacy system

AbstractCloud solutions accelerate the large‐scale acceptance of IoT projects. By diminishing the need for maintaining on‐premises infrastructure, the cloud has enabled corporations to surpass the traditional applications of IoT (e.g., in‐home appliances) and opened the doors for large‐scale deployment of IoT applications on the cloud. However, shifting legacy systems to the cloud environment can be considerably difficult. Accordingly, this article proposes a method that may support organizations in deciding to modernize their legacy systems. The main concept of this study is to discuss the modernization strategies in detail and to support organizations in selecting the most accurate and appropriate cloud migration strategy, based on their requirements of the legacy system. This article introduces a novel research process, called the K‐means cosine cloud clustering method (K3CM). K3CM is a statistical knowledge‐based method for identifying and clustering the most relevant and similar cloud migration strategies. The quality of a cluster is evaluated by measuring intra‐cohesiveness. Simulation experiments statistically analyzed, evaluated, and verified the quality of K3CM clusters. Correspondence analysis explored the similarity and relationship among cloud migration frameworks and validated the proposed technique. The statistical and simulation results of this study focus on the analytics and decision support system implementation that provides a reliable, valid, and robust clustering method for modernizing the legacy system.

Credibility-Supervised Dynamic Clustering and Consensus Model Involving Minority Opinions Handling in Social-Network Large-Scale Decision Making

Existing studies on social-network large-scale decision making (SN-LSDM) have overlooked two vital roles of the information credibility of decision makers (DMs): 1) Information credibility directly determines the reliability of clustering results and final decision output; 2) Consideration of minority opinions may hold the truth but may also be risky, while the credibility analysis of minority opinions can help avoid risks. To this end, this paper proposes credibility-supervised dynamic clustering and consensus model involving minority opinion processing. Firstly, DMs' information is analyzed and preprocessed to obtain DMs' credibility evaluation. Subsequently, DMs' credibility is used as one of the clustering criteria to supervise and guide the trust relationships and the clustering of large-scale group, which assists in deriving the reliable clustering result. This clustering is dynamic because the clustering result may alter as the DMs' information changes in consensus. Afterwards, an SN-LSDM consensus model with minority opinions settlement is developed. A credibility-based mechanism for identification, DMs' discussion and weight adjustment of minority opinion subgroup is constructed, including it trust risk measurement method and trust risk level-based weight adjustment. Then, a risk index is defined in feedback adjustment mechanism to adjust DMs' information, and a decision plan with a higher consensus level is obtained. Finally, the feasibility and effectiveness of the proposed model are verified through the case study of “916” Lu County earthquake relief. Discussions and comparisons are conducted by simulation experiments to explore the capabilities and advantages of our proposal.

Cybersecurity threats mitigation in Internet of Vehicles communication system using reliable clustering and routing

Security and privacy while considering vehicular networks are important research topics. Appropriate clustering algorithms enable long-distance communications in highly dynamic Internet of Vehicles (IoV) systems. IoV system QoS requires constant, low-cost clustering. However, to prevent malicious behaviors, sensitive vehicle networks need an access control system for data security and privacy. The existing solutions suffered from higher complexity and complete reliability. Thus, the main objective of this paper is to propose a lightweight routing technique to express network access control policies to ensure higher reliability with minimum latency and overhead. The Trust Aware Clustering-based Routing Protocol (TACR) solves IoV communication security and reliability issues while reducing computational costs and latency. TACR functionality relies on trust-management methods for optimal Cluster Head (CH) and relay (data forwarder) selection. Each car's direct and indirect trust scores are calculated during clustering. The fitness function of the Ant Colony Optimisation (ACO) algorithm uses each vehicle's hybrid trust value. The ACO algorithm chooses stable vehicles for the best CH. In the routing phase, we select forwarding relays and apply trust-based access control rules to ensure safe and reliable data transmission. Fuzzy logic selects the optimal relay node and sets access control rules based on trust evaluation. The trust criteria consider each vehicle's expertise, knowledge, and suggestions throughout. The simulation findings reveal that the TACR protocol outperforms state-of-the-art protocols significantly. Average throughput and PDR are increased by 14.17 % and 14.34 % using TACR, respectively. Average delay and communication overhead were reduced by 33.84 % and 14.28 % using the TACR protocol, respectively.

Survey for Distant Stellar Aggregates in the Galactic Disk: Detecting 2000 Star Clusters and Candidates, along with the Dwarf Galaxy IC 10

Despite having data for over 109 stars from Gaia, less than 104 star clusters and candidates have been discovered. In particular, distant star clusters are rarely identified, due to the challenges posed by heavy extinction and great distance. However, Gaia data has continued to improve, enabling even fainter cluster members to be distinguished from field stars. In this work, we introduce a star-cluster search method based on the DBSCAN algorithm; we have made improvements to make it better suited for identifying clusters on dimmer and more distant stars. After having removed member stars of known Gaia-based clusters, we identified 2086 objects with ∣b∣ < 10°, of which 1488 are highly reliable open star clusters, along with 569 candidates, 28 globular cluster candidates, and one irregular galaxy (IC 10) at low Galactic latitudes. We found that the proper motion of IC 10 is similar to, yet slightly different from, the water maser observations, which is an important result for the comparison with Gaia and the Very Long Baseline Array. When compared with the star clusters appearing in Gaia Data Release (DR) 2/EDR3, we found nearly 3 times as many new objects above a distance of 5 kpc, including hundreds of them above A v > 5 mag. This has enabled us to detect a higher number of old clusters, over a billion years old, that are difficult to detect due to observational limitations. Our findings significantly expand the remote cluster sample and enhance our understanding of the limits of Gaia DR3 data in stellar aggregates research.

Energy balanced reliable and effective clustering for underwater wireless sensor networks

While sending data from a submerged wireless sensor network, more power is necessary due to the additional distance the signal must travel as well as the higher amount of energy lost along the route. Also, the acoustic medium generated by the shadow and the acoustic animals alters the transmission between the source and destination nodes. Multipath fading, significant path loss, and various interferences are some of the difficulties that come with communication at larger depths. The connection strength was variable, there was a low bandwidth, a high salt content, high pressure, high temperature, a sinkhole, and the route curved. These variables influence a variety of network aspects, including energy efficiency, link quality, and packet delivery ratio (PDR). The Energy Balanced Reliable and Effective Clustering (EBREC) method was created to ensure that data packets reach at their destination intact. This was done in order to avoid the complications caused by these considerations. PDR and transmission loss reduction are other targets of the presented effort. The anticipated EBREC’s performance is assessed by comparing it to many already defined benchmarks. According to the evaluation report, the simulation results reveal that EBREC performs very well when compared to the other methods in terms of Residual Energy of 0.615 J, Consumed Energy of 2.3 mJ, Throughput of 10Mbps, PDR of 97.6 %, Network Lifetime of 1750 sec, Number of Alive Nodes of 91%. Hence, the simulation results reveal that the EBREC technique beats competing approaches.

Reliability of online dental final exams in the pre and post COVID-19 era: A comparative study.

Amidst the fourth COVID-19 wave in Viet Nam, national lockdowns necessitated the closure of numerous dental schools. To assess DDS (Doctor of Dental Surgery) graduation exams, this study analyzed their 2021 implementation in comparison to onsite exams conducted in 2020 and 2022 at the Faculty of Odonto-Stomatology, University of Medicine and Pharmacy at Ho Chi Minh City, Viet Nam (FOS-UMPH). The final online examination comprises two main sessions: a synchronous online examination using FOS-UMPH e-Learning for theories (consisting of 200 MCQs and 3 written tests with 3 clinical situations needed be solved) and a synchronous online examination using Microsoft Teams for practicum (comprising of 12 online OSCE stations). The final grades were evaluated using the same metrics in face-to-face final examinations in 2022 and 2020. A total of 114, 112 and 95 students were recruited for the first-time exams in 2020, 2021 and 2022, respectively. In order to analyze the reliability, histogram and k-mean clustering were employed. The histograms from 2020, 2021 and 2022 showed a striking similarity. However, fewer students failed in 2021 and 2022 (13% and 12.6%, respectively) compared to 2020 (28%), with clinical problem-solving part grades (belonging to theory session) being notably higher in 2021 and 2022. Intriguingly, the MCQ Score results showed the identical patterns. The courses of orthodontics, dental public health, and pediatrics subjects (in the group of prevention and development dentistry) stood out for their exceptional accuracy across both sessions. After examining data gathered over three years, we identified three distinct clusters: the first comprised of scattered average and low scores, the second characterized by high scores but unstable and scattered and the third cluster boasting consistently high and centered scores. According to our study, online and onsite traditional graduation exam results are relatively equivalent, but additional measures are necessary to standardize the final examination and adapt to the new normal trend in dental education.

Improving the open cluster census

Context.Data from theGaiasatellite are revolutionising our understanding of the Milky Way. With every new data release, there is a need to update the census of open clusters.Aims.We aim to conduct a blind, all-sky search for open clusters using 729 million sources fromGaiaDR3 down to magnitudeG ∼ 20, creating a homogeneous catalogue of clusters including many new objects.Methods.We used the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm to recover clusters. We validated our clusters using a statistical density test and a Bayesian convolutional neural network for colour-magnitude diagram classification. We inferred basic astrometric parameters, ages, extinctions, and distances for the clusters in the catalogue.Results.We recovered 7167 clusters, 2387 of which are candidate new objects and 4782 of which crossmatch to objects in the literature, including 134 globular clusters. A more stringent cut of our catalogue contains 4105 highly reliable clusters, 739 of which are new. Owing to the scope of our methodology, we are able to tentatively suggest that many of the clusters we are unable to detect may not be real, including 1152 clusters from the Milky Way Star Cluster (MWSC) catalogue that should have been detectable inGaiadata. Our cluster membership lists include many new members and often include tidal tails. Our catalogue’s distribution traces the galactic warp, the spiral arm structure, and the dust distribution of the Milky Way. While much of the content of our catalogue contains bound open and globular clusters, as many as a few thousand of our clusters are more compatible with unbound moving groups, which we will classify in an upcoming work.Conclusions.We have conducted the largest search for open clusters to date, producing a single homogeneous star cluster catalogue which we make available with this paper.