Membership Study of Open Cluster NGC 6134 using HDBSCAN Clustering Algorithm

In this study, we carried out CCD UBV photometry of eight open clusters, ASCC 115, Collinder 421, NGC 6793, NGC 7031, NGC 7039, NGC 7086, Roslund 1, Stock 21, and determined their reddening, metallicity, distance, age, and mass functions. We used new Gaia Data Release 2 (DR2) astrometric data to separate cluster member stars from the field stars and obtain precise structural and astrophysical parameters. To identify cluster member stars we utilized an unsupervised membership assignment code (UPMASK), which is based on the photometric and astrometric data. The density distributions for the open clusters show good fits with the empirical King model except for Roslund 1 and Stock 21 not having central concentration. The colour excesses and metallicities were derived separately using U-B vs B-V two-colour diagrams. Keeping these parameters as constants, we simultaneously calculated distance moduli and ages of the clusters from V vs B-V and V vs U-B colour-magnitude diagrams using PARSEC theoretical isochrones. Taking into account Gaia DR2 proper motion components and parallaxes of the member stars, we also calculated mean proper motions and distances for the clusters. Distances derived both from isochrone fitting to colour-magnitude diagrams of the clusters and Gaia DR2 trigonometric parallaxes are compatible with each other. Slopes of the mass functions of the eight open clusters are in good agreement with Salpeter (1955) value of 1.35.

In this paper, we use two methods, an algorithm of the density-based spatial clustering of applications with noise combined with the Gaussian mixture model, and the maximum likelihood method, to study two open clusters: IC 2488 and IC 2714 with Gaia Data Release 3 data. We compare the two methods in terms of cluster star number, isochrone fitting, cluster mass, and radius. The comparison shows that the selections of cluster stars by the two methods are consistent. The parameters obtained by the two methods are very close. But there are slight differences between the two methods for faint stars in IC 2714. The maximum likelihood method selects more faint stars outside of the main-sequence stripe. We conclude that the maximum likelihood method is more likely affected by field stars.

The interplay between political campaigning and online discourse has emerged as a critical area of computational social science, particularly concerning the propagation of hate speech. This study investigates how Donald Trump’s 2024–2025 U.S. presidential campaign influenced the prevalence and thematic focus of hate speech on Reddit, a platform known for its politically engaged communities. Leveraging a dataset of over 55 million Reddit posts spanning two key periods (pre- and mid-campaign), we develop a large-scale Natural Language Processing (NLP) methodology to detect and analyse the dynamics of hate speech. Hate speech is identified using a fine-tuned BERT model optimized for multi-class classification, distinguishing hateful, offensive, and neutral content. To explore the structure of hate speech targets, we apply Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), a density-based clustering algorithm that accommodates variable data distributions in high-dimensional embeddings. A post-processing algorithm further refines overlapping categories by decomposing identity-based slurs into distinct subgroups. Our findings reveal a marked increase in hate speech volume during the campaign, along with significant shifts in targeted identity clusters, particularly toward ideological, gendered, and racial groups. These results contribute empirical evidence to debates around algorithmic content moderation, political communication, and the computational modeling of social polarization.

Traditional searchable encryption schemes construct document vectors based on the term frequency-inverse document frequency (TF-IDF) model. Such vectors are not only high-dimensional and sparse but also ignore the semantic information of the documents. The Sentence Bidirectional Encoder Representations from Transformers (SBERT) model can be used to train vectors containing document semantic information to realize semantic-aware multi-keyword search. In this paper, we propose a privacy-preserving searchable encryption scheme based on the SBERT model. The SBERT model is used to train vectors containing the semantic information of documents, and these document vectors are then used as input to the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) clustering algorithm. The HDBSCAN algorithm generates a soft cluster membership vector for each document. We treat each cluster as a topic, and the vector represents the probability that the document belongs to each topic. According to the clustering process in the schemes, the topic-term frequency-inverse topic frequency (TTF-ITF) model is proposed to generate keyword topic vectors. Through the SBERT model, searchable encryption scheme can achieve more precise semantic-aware keyword search. At the same time, the special index tree is used to improve search efficiency. The experimental results on real datasets prove the effectiveness of our scheme.

Arecaceae (palms) are an important resource for indigenous communities as well as fauna populations across Amazonia. Understanding the spatial patterns and the environmental factors that determine the habitats of palms is of considerable interest to rainforest ecologists. Here, we utilize remotely sensed imagery in conjunction with topography and soil attribute data and employ a generalized cluster identification algorithm, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), to study the underlying patterns of palms in two areas of Guyana, South America. The results of the HDBSCAN assessment were cross-validated with several point pattern analysis methods commonly used by ecologists (the quadrat test for complete spatial randomness, Morista Index, Ripley’s L-function, and the pair correlation function). A spatial logistic regression model was generated to understand the multivariate environmental influences driving the placement of cluster and outlier palms. Our results showed that palms are strongly clustered in the areas of interest and that the HDBSCAN’s clustering output correlates well with traditional analytical methods. The environmental factors influencing palm clusters or outliers, as determined by logistic regression, exhibit qualitative similarities to those identified in conventional ground-based palm surveys. These findings are promising for prospective research aiming to integrate remote flora identification techniques with traditional data collection studies.

Aphasia assessment plays a crucial role in rehabilitating people with aphasia (PWA), including classifying healthy individuals, identifying subtypes of aphasia, and assessing severity. This study explores unsupervised clustering methods in aphasic speech data, comparing five feature extraction approaches and four clustering algorithms. Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), Agglomerative clustering, K-means, and Gaussian Mixture Models. The focus is on distinguishing between fluent and non-fluent aphasia and subtypes such as Broca's, Wernicke's, and anomic aphasia. The feature extraction methods include Mel-Frequency Cepstral Coefficients (MFCCs), OpenL3 embeddings, their combination, and a fusion of MFCC with chroma features, spectral contrast, zero-crossing rate, and a novel deep convolutional autoencoder-based (CAE) model. These methods are evaluated for their ability to capture key characteristics of aphasic speech, with the clustering results assessed using intrinsic evaluation metrics. Our findings highlight the effectiveness of different feature extraction methods, with CAE emerging as the most effective approach in distinguishing aphasic speech categories and improving clustering performance. This research highlights the potential of unsupervised learning in aphasia research and offers insight into automated diagnosis and speech therapy tools, contributing to enhanced aphasia assessment and rehabilitation.

This research introduces an enhanced stratified sampling-density-based spatial clustering of applications with noise (SS-DBSCAN), a scalable and robust density-based clustering algorithm designed to tackle challenges in high-dimensional and complex data analysis. The algorithm integrates advanced parameter optimization techniques to improve clustering accuracy and interpretability. Key innovations include a fast grid search method for optimizing the search of optimal minimum points (MinPts) by keeping the ϵ parameter obtained constant. Notably, this study emphasizes the often-overlooked MinPts parameter, introducing a dynamic approach that initiates by calculating density metrics within a specified ϵ distance and adjusting the MinPts range based on the standard deviation of these metrics. This approach identifies optimal MinPts values based on the maximum allowed range. Comprehensive experiments on five real-world datasets demonstrate SS-DBSCAN’s superior performance compared to density-based spatial clustering of applications with noise (DBSCAN), hierarchical DBSCAN, and ordering points to identify the clustering structure (OPTICS), evidenced by higher silhouette and Davies–Bouldin index scores. The results highlight SS-DBSCAN’s ability to capture intrinsic clustering structures accurately, providing deeper insights across various research domains. SS-DBSCAN’s scalability and adaptability to diverse data densities make it a valuable tool for analyzing large, complex datasets.

In situ mineralogical and chemical analyses of rock samples using a space-prototype laser ablation ionization mass spectrometer along with unsupervised machine learning are powerful tools for the study of surface samples on planetary bodies. This potential is demonstrated through the examination of a thin section of a terrestrial rock sample in the laboratory. Autonomous isolation of mineral phases within the acquired mass spectrometric data is achieved with two dimensionality reduction techniques: uniform manifold approximation and projection (UMAP) and density-preserving variation of UMAP (densMAP), and the density-based clustering algorithm Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). Both densMAP and UMAP yield comparable outcomes, successfully isolating the major mineral phases fluorapatite, calcite, and forsterite in the studied rock sample. Notably, densMAP reveals additional insights into the composition of the sample through outlier detection, uncovering signals from the trace minerals pyrite, rutile, baddeleyite, and uranothorianite. Through a grid search, the stability of the methods over a broad model parameter space is confirmed, revealing a correlation between the level of data preprocessing and the resulting clustering quality. Consequently, these methods represent effective strategies for data reduction, highlighting their potential application on board spacecraft to obtain direct and quantitative information on the chemical composition and mineralogy of planetary surfaces and to optimize mission returns through the unsupervised selection of valuable data.

Visualising patterns in clinicians' eye movements while interpreting fetal ultrasound imaging videos is challenging. Across and within videos, there are differences in size an d position of Areas-of-Interest (AOIs) due to fetal position, movement and sonographer skill. Currently, AOIs are manually labelled or identified using eye-tracker manufacturer specifications which are not study specific. We propose using unsupervised clustering to identify meaningful AOIs and bi-contour plots to visualise spatio-temporal gaze characteristics. We use Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to identify the AOIs, and use their corresponding images to capture granular changes within each AOI. Then we visualise transitions within and between AOIs as read by the sonographer. We compare our method to a standardised eye-tracking manufacturer algorithm. Our method captures granular changes in gaze characteristics which are otherwise not shown. Our method is suitable for exploratory data analysis of eye-tracking data involving multiple participants and AOIs.

Automatic individual recognition of wild Crested Ibis based on hybrid method of self-supervised learning and clustering

Aiming at the trajectory clustering problem of observed data, an improved Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm, named FB-HDBSCAN, is proposed. Firstly, the spatiotemporal features of the data are thoroughly exploited to construct a distance matrix based on Fast Dynamic Time Warping (FastDTW), ensuring an accurate measurement of similarity between data points. Then, the Bayesian Optimization (BO) algorithm is introduced for adaptive parameter optimization, and the optimal parameters, along with the distance matrix, are input into the HDBSCAN algorithm framework to achieve automatic clustering of trajectory data. Experimental results demonstrate that the proposed algorithm achieves superior performance across multiple datasets, validating its feasibility and applicability.

Machine Learning has found application in solving complex problems in different fields of human endeavors such as intelligent gaming, automated transportation, cyborg technology, environmental protection, enhanced health care, innovation in banking and home security, and smart homes. This research is motivated by the need to explore the global structure of machine learning to ascertain the level of bibliographic coupling, collaboration among research institutions, co-authorship network of countries, and sources coupling in publications on machine learning techniques. The Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) was applied to clustering prediction of authors dominance ranking in this paper. Publications related to machine learning were retrieved and extracted from the Dimensions database with no language restrictions. Bibliometrix was employed in computation and visualization to extract bibliographic information and perform a descriptive analysis. VOSviewer (version 1.6.16) tool was used to construct and visualize structure map of source coupling networks of researchers and co-authorship. About 10,814 research papers on machine learning published from 2010 to 2020 were retrieved for the research. Experimental results showed that the highest degree of betweenness centrality was obtained from cluster 3 with 153.86 from the University of California and Harvard University with 24.70. In cluster 1, the national university of Singapore has the highest degree betweenness of 91.72. Also, in cluster 5, the University of Cambridge (52.24) and imperial college London (4.52) having the highest betweenness centrality manifesting that he could control the collaborative relationship and that they possessed and controlled a large number of research resources. Findings revealed that this work has the potential to provide valuable guidance for new perspectives and future research work in the rapidly developing field of machine learning.

The coronavirus disease 2019 (COVID-19) pandemic presented unique challenges to the U.S. healthcare system, particularly for nonprofit U.S. hospitals that are obligated to provide community benefits in exchange for federal tax exemptions. We sought to examine how hospitals initiated, modified, or disbanded community benefits programming in response to the COVID-19 pandemic. We used the free-response text in Part IV of Internal Revenue Service (IRS) Form 990 Schedule H (F990H) to assess health equity and disparities. We combined traditional key term frequency and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) clustering approaches with a novel Generative Pre-trained Transformer (GPT) 3.5 summarization approach. Our research reveals shifts in community benefits programming. We observed an increase in COVID-related terms starting in the 2019 tax year, indicating a pivot in community focus and efforts toward pandemic-related activities such as telehealth services and COVID-19 testing and prevention. The clustering analysis identified themes related to COVID-19 and community benefits. Generative Artificial Intelligence (GenAI) summarization with GPT3.5 contextualized these changes, revealing examples of healthcare system adaptations and program cancellations. However, GPT3.5 also encountered some accuracy and validation challenges. This multifaceted text analysis underscores the adaptability of hospitals in maintaining community health support during crises and suggests the potential of advanced AI tools in evaluating large-scale qualitative data for policy and public health research.

We explored the use of broadband colors to classify stars, galaxies, and quasi-stellar objects (QSOs). Specifically, we applied sharpened dimensionality reduction (SDR)-aided classification to this problem, with the aim of enhancing cluster separation in the projections of high-dimensional data clusters to allow for better classification performance and more informative projections. The main objective of this work was to apply SDR to large sets of broadband colors derived from the CPz catalog to obtain projections with clusters of star, galaxy, and QSO data that exhibit a high degree of separation. The SDR method achieves this by combining density-based clustering with conventional dimensionality-reduction techniques. To make SDR scalable and have the ability to project samples using the earlier-computed projection, we used a deep neural network trained to reproduce the SDR projections. Subsequently classification was done by applying a $k$-nearest neighbors ($k$-NN) classifier to the sharpened projections. Based on a qualitative and quantitative analysis of the embeddings produced by SDR, we find that SDR consistently produces accurate projections with a high degree of cluster separation. A number of projection performance metrics are used to evaluate this separation, including the trustworthiness, continuity, Shepard goodness, and distribution consistency metrics. Using the $k$-NN classifier and consolidating the results of various data sets, we obtain precisions of 99.7<!PCT!>, 98.9<!PCT!>, and 98.5<!PCT!> for classifying stars, galaxies, and QSOs, respectively. Furthermore, we achieve completenesses of 97.8<!PCT!>, 99.3<!PCT!>, and 86.8<!PCT!>, respectively. In addition to classification, we explore the structure of the embeddings produced by SDR by cross-matching with data from Gaia DR3, Galaxy Zoo 1, and a catalog of specific star formation rates, stellar masses, and dust luminosities. We discover that the embeddings reveal astrophysical information, which allows one to understand the structure of the high-dimensional broadband color data in greater detail. We find that SDR-aided star, galaxy, and QSO classification performs comparably to another unsupervised learning method using hierarchical density-based spatial clustering of applications with noise (HDBSCAN) but offers advantages in terms of scalability and interpretability. Furthermore, it outperforms traditional color selection methods in terms of QSO classification performance. Overall, we demonstrate the potential of SDR-aided classification to provide an accurate and physically insightful classification of astronomical objects based on their broadband colors.

Context. With an unprecedented astrometric and photometric data precision, Gaia EDR3 provides, for the first time, the opportunity to systematically detect and map, in the optical bands, the low-mass populations of the star forming regions (SFRs) in the Milky Way. Aims. We aim to provide a catalogue of the Gαiα EDR3 data (photometry, proper motions and parallaxes) of the young stellar objects (YSOs) identified in the Galactic plane |b| < 30°) within about 1.5 kpc. The catalogue of the SFRs to which they belong is also provided to study the properties of the very young clusters and put them in the context of the Galaxy structure. Methods. We applied the machine learning unsupervised clustering algorithm density-based spatial clustering of applications with noise (DBSCAN) to a sample of Gaia EDR3 data photometrically selected on the region where very young stars (t ≲ 10 Myr) are expected to be found, with the aim of identifying co-moving and spatially consistent stellar clusters. A sub-sample of 52 clusters, selected among the 7 323 found with DBSCAN, has been used as template data set to identify very young clusters from the pattern of the observed colour-absolute magnitude diagrams through a pattern-match process. Results. We find 124440 candidate YSOs clustered in 354 SFRs and stellar clusters younger than 10 Myr and within ≲ 1.5 kpc. In addition, 65 863 low-mass members of 322 stellar clusters located within ~500pc and with ages 10Myr ≲ t ≲ 100 Myr were also found. Conclusions. The selected YSOs are spatially correlated with the well-known SFRs. Most of them are associated with well-concentrated regions or complex structures of the Galaxy, and a substantial number of them have been recognised for the first time. The massive SFRs, such as, for example, Orion, Sco-Cen, and Vela, located within 600–700 pc trace a very complex three-dimensional pattern, while the farthest ones seem to follow a more regular pattern along the Galactic plane.