Cluster Centroids Research Articles

UNCA: A Neutrosophic-Based Framework for Robust Clustering and Enhanced Data Interpretation

Accurately representing the complex linkages and inherent uncertainties included in huge datasets is still a major difficulty in the field of data clustering. We address these issues with our proposed Unified Neutrosophic Clustering Algorithm (UNCA), which combines a multifaceted strategy with Neutrosophic logic to improve clustering performance. UNCA starts with a full-fledged similarity examination via a λ-cutting matrix that filters meaningful relationships between each two points of data. Then, we initialize centroids for Neutrosophic K-Means clustering, where the membership values are based on their degrees of truth, indeterminacy and falsity. The algorithm then integrates with a dynamic network visualization and MST (Minimum Spanning Tree) so that a visual interpretation of the relationships between the clusters can be clearly represented. UNCA employs Single-Valued Neutrosophic Sets (SVNSs) to refine cluster assignments, and after fuzzifying similarity measures, guarantees a precise clustering result. The final step involves solidifying the clustering results through defuzzification methods, offering definitive cluster assignments. According to the performance evaluation results, UNCA outperforms conventional approaches in several metrics: it achieved a Silhouette Score of 0.89 on the Iris Dataset, a Davies-Bouldin Index of 0.59 on the Wine Dataset, an Adjusted Rand Index (ARI) of 0.76 on the Digits Dataset, and a Normalized Mutual Information (NMI) of 0.80 on the Customer Segmentation Dataset. These results demonstrate how UNCA enhances interpretability and resilience in addition to improving clustering accuracy when contrasted with Fuzzy C-Means (FCM), Neutrosophic C-Means (NCM), as well as Kernel Neutrosophic C-Means (KNCM). This makes UNCA a useful tool for complex data processing tasks.

SEMI-AUTOMATED EXTRACTION OF RESEARCH TOPICS AND TRENDS FROM NCI FUNDING IN RADIOLOGICAL SCIENCES FROM 2000-2020.

Investigators and funding organizations desire knowledge on topics and trends in publicly funded research but current efforts for manual categorization have been limited in breadth and depth of understanding. We present a semi-automated analysis of 21 years of R-type National Cancer Institute (NCI) grants to departments of radiation oncology and radiology using natural language processing (NLP). We selected all non-education R-type NCI grants from 2000 to 2020 awarded to departments of radiation oncology/radiology with affiliated schools of medicine. We used pre-trained word embedding vectors to represent each grant abstract. A sequential clustering algorithm assigned each grant to one of 60 clusters representing research topics; we repeated the same workflow for 15 clusters for comparison. Each cluster was then manually named using the top words and closest documents to each cluster centroid. The interpretability of document embeddings was evaluated by projecting them onto 2 dimensions. Changes in clusters over time were used to examine temporal funding trends. We included 5,874 grants totaling 1.9 billion dollars of NCI funding over 21 years. The human-model agreement was similar to the human interrater agreement. Two-dimensional projections of grant clusters showed two dominant axes: physics-biology and therapeutic-diagnostic. Therapeutic and physics clusters have grown faster over time than diagnostic and biology clusters. The three topics with largest funding increase were Imaging biomarkers, Informatics, and Radiopharmaceuticals, which all had a mean annual growth of >$218,000. The three topics with largest funding decrease were Cellular stress response, Advanced imaging hardware technology, and Improving performance of breast cancer computer-aided detection (CAD), which all had a mean decrease of >$110,000. We developed a semi-automated NLP approach to analyze research topics and funding trends. We applied this approach to NCI funding in the radiological sciences to extract both domains of research being funded and temporal trends.

SCL: Selective Contrastive Learning for Data-driven Zero-shot Relation Extraction

Abstract Relation extraction has evolved from supervised relation extraction to zero-shot setting due to the continuous emergence of newly generated relations. Some pioneering works handle zero-shot relation extraction by reformulating it into proxy tasks, such as reading comprehension and textual entailment. Nonetheless, the divergence in proxy task formulations from relation extraction hinders the acquisition of informative semantic representations, leading to subpar performance. Therefore, in this paper, we take a data-driven view to handle zero-shot relation extraction under a three-step paradigm, including encoder training, relation clustering, and summarization. Specifically, to train a discriminative relational encoder, we propose a novel selective contrastive learning framework, namely, SCL, where selective importance scores are assigned to distinguish the importance of different negative contrastive instances. During testing, the prompt-based encoder is employed to map test samples into representation vectors, which are then clustered into several groups. Typical samples closest to the cluster centroid are selected for summarization to generate the predicted relation for all samples in the cluster. Moreover, we design a simple non-parametric threshold plugin to reduce false-positive errors in inference on unseen relation representations. Our experiments demonstrate that SCL outperforms the current state-of-the-art method by over 3% across all metrics.

Pose Analysis in Free-Swimming Adult Zebrafish, Danio rerio: “Fishy” Origins of Movement Design

Introduction: Movement requires maneuvers that generate thrust to either make turns or move the body forward in physical space. The computational space for perpetually controlling the relative position of every point on the body surface can be vast. We hypothesize the evolution of efficient design for movement that minimizes active (neural) control by leveraging the passive (reactive) forces between the body and the surrounding medium at play. To test our hypothesis, we investigate the presence of stereotypical postures during free-swimming in adult zebrafish, Danio rerio. Methods: We perform markerless tracking using DeepLabCut (DLC), a deep learning pose-estimation toolkit, to track geometric relationships between body parts. We identify putative clusters of postural configurations from twelve freely behaving zebrafish, using unsupervised multivariate time-series analysis (B-SOiD machine-learning software) and of distances and angles between body segments extracted from DLC data. Results: When applied to single individuals, DLC-extracted data reveal a best-fit for 36–50 clusters in contrast to 86 clusters for data pooled from all 12 animals. The centroids of each cluster obtained over 14,000 sequential frames represent an a priori classification into relatively stable “target body postures.” We use multidimensional scaling of mean parameter values for each cluster to map cluster centroids within two dimensions of postural space. From a posteriori visual analysis, we condense neighboring postural variants into 15 superclusters or core body configurations. We develop a nomenclature specifying the anteroposterior level/s (upper, mid, and lower) and degree of bending. Conclusion: Our results suggest that constraining bends to mainly three anteroposterior levels in fish paved the way for the evolution of a neck, fore- and hind limb design for maneuverability in land vertebrates.

Enhancing Clustering Stability and Efficiency: A Framework for Optimizing K-means, K-medoids, and K-shape with Intelligent Algorithms

Clustering methods like Kmeans often produce inconsistent results due to the random initialization of cluster centroids, even with optimizations such as Kmeans++, which improve centroid selection but fail to eliminate sensitivity to initialization randomness. Additionally, the choice of the number of clusters and distance metrics significantly impacts clustering performance. This paper proposes an enhanced framework combining intelligent optimization algorithms—Sparrow Search Algorithm (SSA), Dung Beetle Optimizer (DBO), and Sine Cosine Algorithm (SCA)—to optimize clustering outcomes for Kmeans, Kmedoids, and Kshape. The framework also incorporates dimensionality reduction techniques, including Principal Component Analysis (PCA), Non-Negative Matrix Factorization (NNMF), and Singular Value Decomposition (SVD), to address high-dimensional data challenges. Experimental results on benchmark datasets demonstrate the proposed framework’s effectiveness, with SSA achieving the highest silhouette score of 0.68 and reducing runtime by 35% compared to traditional methods. This approach enhances clustering stability and accuracy, offering a robust solution for diverse applications.

Pengelompokkan daerah rawan bencana di kabupaten Boyolali menggunakan algoritma k-means

The country of Indonesia, which is located in the Southeast Asia region, faces a high challenge of disasters due to demographic, geological and geographical factors that influence the occurrence of disasters, both those caused by natural, non-natural and human factors. In the Province of Central Java, especially Boyolali, disasters pose a significant threat, especially when the weather is uncertain. However, to identify priority areas for the lowest and highest levels of natural disasters in Boyolali, it is necessary to improve using data mining methods, therefore to facilitate analysis and grouping of data in identifying disaster-prone areas in Boyolali, the K-Means clustering algorithm is used. The method used in this research is qualitative and the data is analyzed using the Rapidminer Studio Ver application. 9.10. Based on the results of the analysis, there is a division of disaster-prone areas into 3 clusters in Boyolali Regency, namely very high vulnerability, medium vulnerability, and low vulnerability. The results of calculations with Rapidminer show that there are 3 sub-districts in cluster 2, 2 sub-districts in cluster 1, and 17 sub-districts in cluster 0. It should be noted that manual calculations can produce differences because the cluster center point or centroid is taken randomly for each calculation method. It is hoped that this grouping of areas can be used as a support for the Boyolali regional BPBD to focus on anticipatory steps to reduce the impact of disasters on society that may occur in the future.

The development of classification-based machine-learning models for the toxicity assessment of chemicals associated with plastic packaging

Assessing chemical toxicity in materials like plastic packaging is critical to safeguarding public health. This study presents the development of classification-based machine learning models to predict the toxicity of chemicals associated with plastic packaging. Using an extensive dataset of chemical structures, we trained multiple machine learning models—Random Forest, Support Vector Machine, Linear Discriminant Analysis, and Logistic Regression—targeting endpoints such as Neurotoxicity, Hepatotoxicity, Dermatotoxicity, Carcinogenicity, Reproductive Toxicity, Skin Sensitization, and Toxic Pneumonitis. The dataset was pre-processed by selecting 2D molecular descriptors as feature inputs, with resampling methods (ADASYN, Borderline SMOTE, Random Over-sampler, SVMSMOTE Cluster Centroid, Near Miss, Random Under Sampler) applied to balance classes for accurate classification. A five-fold cross-validation technique was used to optimize model performance, with model parameters fine-tuned using grid search. The model performance was evaluated using accuracy (Acc), sensitivity (Se), specificity (Sp), and area under the receiver operating characteristic curve (AUC-ROC) metrics. In most of the cases, the model accuracy was 0.8 or above for both training and test sets. Additionally, SHAP (SHapley Additive exPlanations) values were utilized for feature importance analysis, highlighting significant descriptors contributing to toxicity predictions. The models were ranked using the Sum of Ranking Differences (SRD) method to systematically select the most effective model. The optimal models demonstrated high predictive accuracy and interpretability, providing a scalable and efficient solution for toxicity assessment compared to traditional methods. This approach offers a valuable tool for rapidly screening potentially hazardous chemicals in plastic packaging.

An integrated multistage ensemble machine learning model for fraudulent transaction detection

Fraudulent transactions continue to pose a concern for financial institutions and organizations, necessitating the development of effective detection tools. Identification and prevention of fraudulent transactions depend heavily on the detection of credit card fraud. Even though instances of credit card fraud are uncommon, they can nonetheless cause significant financial losses because of the high cost of fraudulent transactions. When fraud is discovered early on, investigators can act quickly to stop additional losses. But because the investigation process takes a while, there are only so many warnings that can be looked through in detail in a given day. Thus, a fraud detection model’s main goal is to minimize false alarms and missed fraud situations while producing accurate alerts. To improve fraud identification, we provide in this study an integrated multistage ensemble Machine Learning (IMEML) model that incorporates various multistage ensemble models intelligently, such as Ensemble Independent Classifier (EIC), Ensemble Bagging Classifier (EBC), and Ensemble ML Classifier (EMC). In order to overcome the problem of data imbalance, we use a number of methods-including Instant Hardness Threshold with EMC (IHT+EMC), Cluster Centroids (CC), and Randon Under Sampler (RUS)-that go beyond traditional methods. We run our studies on a 284,807-transaction credit card dataset that is made available to the public. The accuracy rates of 99.94%, 99.91%, 99.14%, 99.52%, and perfect 100% for accuracy, precision, recall, f1-score, and AUC score, respectively, are achieved by the suggested model, demonstrating remarkable performance scores. For real-world fraud detection applications, the EIBMC model sets a new benchmark for identifying fraudulent transactions in high-frequency scenarios by outperforming cutting-edge techniques.

A novel multi‐target TBD scheme for GNSS‐based passive bistatic radar

AbstractGlobal Navigation Satellite System (GNSS)‐based passive bistatic radar (PBR) systems hold promise for use as low‐altitude surveillance mechanisms in critical urban and suburban zones, attributed to their low power consumption, good concealment, and worldwide reach. However, the increasing demand for airspace regulation presents challenges for multi‐target tracking. Additionally, the limited power budget of GNSS signals results in low target SNR, restricting the detection range. Hence, a novel multi‐target track‐before‐detect (TBD) scheme is proposed. This strategy employs a dual‐channel coarse focusing (DCCF)‐based cluster centroid extraction algorithm to identify potential target information in the range Doppler (RD) domain. Subsequently, a modified cardinalised probability hypothesis density (MCPHD) filter is utilised, enhanced with a birth target intensity estimation module assisted by Doppler and a trajectory management module, to accurately track multiple targets under conditions of low SNR. Simulation results and performance analysis using the Optimal Sub‐pattern Assignment (OSPA) metric confirm the effectiveness of our approach. Furthermore, in a real‐world experiment using the GPS L5 signal as an illuminator, the authors successfully processed experimental data to track a civil aircraft over 10 frames, demonstrating the practical applicability of the proposed method in GNSS‐based PBR systems.

Energy efficient routing for improving lifetime in MWSN: A clustering approach

A Mobile wireless sensor network (MWSN) consists of mobile sensor nodes, which can be deployed in any specific environment, and due to its’ mobility it can perform with rapid topological transformations of a network. The sensor nodes having limited battery power are used to collect specific data and this raw data is sent to a static sink node of the network. Under such a scenario, to avoid frequent disconnections due to topological change in the network and can avail more reliable data transmissions in energy awareness perspective, an energy efficient routing protocol for MWSNs to improve its lifetime is proposed here by utilizing a clustering approach. A MWSN with random number of sensor nodes are initially considered and then, clustering algorithm K-means is used to determine a predefined number of clusters with their initial cluster heads (CHs) and centroid locations of these clusters is also determined. The role of these CHs is to elect our DDBLACH (distance to sink and cluster centroid with battery level aware cluster head) nodes from each cluster, by sending and receiving intra-cluster messages among other member sensor nodes. A DDBLACH node is determined by using three factors, such as minimum distance from cluster centroid location, minimum distance from sink and the maximum battery level of the node from each cluster. These DDBLACH nodes are used to collect data from intra-cluster sensors and thereafter, send those towards sink node for further processing using tree-based hierarchical routes. Finally, an energy efficient routing technique for MWSNs is proposed for data transmission from DDBLACH nodes of clusters to sink of the network. Simulation results indicate the superiority of our proposed scheme over other existing methods in various aspects, such as improving more data packet transmission by 14%-23%, presence of alive nodes and subsequently average network lifetime by 5%-24%.

IUCN's Application of Cluster Analysis to Usage Classification Areas in Korean protected areas: Based on Gangwon Province

Background and objective: Protected areas in South Korea are currently designated by different ministries for similar purposes, causing overlapping management and conflicts of interest. Moreover, protected areas after designation have been lacking sufficient management. Thus, it is necessary to classify the types of protected areas in South Korea considering variables related to designation, management, and the natural environment, and to verify the applicability of IUCN categories.Methods: We selected variables that are easy to apply to spatial data, easy to interpret, and can solve problems within protected areas based on previous studies and literature review. The optimal number of clusters was derived by conducting hierarchical and non-hierarchical cluster analysis on 174 protected areas using the selected variables. The applicability of IUCN categories was verified based on the results.Results: Cluster centroids represent the characteristics of the variables in each cluster, which were applied to IUCN categories for classification. Based on this standard, we formed clusters of similar protected areas through cluster analysis and classified them according to IUCN categories. The clusters were compared with the protected areas classified by the existing IUCN categories and were further subdivided according to the characteristics of the use districts.Conclusion: This study classified protected areas located in Gangwon Province as well as overlapping protected areas using criteria such as the natural environment, activity restrictions, protected target and area as set in the research method, and derived the characteristics of each cluster. This study has significance in that it verified the protected areas classified as IUCN categories and suggested appropriate management measures for unclear overlapping protected areas.

Non-elastic time series fuzzy clustering for efficient analysis of industrial data sets

In line with the green transition and digitalization trends, there is an increasing need for effective performance and health monitoring of production machines. Machine operation footprints resonate in quantities such as supply current and pneumatic line pressure, providing valuable insights when analyzed. Examining these low-level signals allows for monitoring and identification of changes in machine operation over time. A critical step in analyzing machine data is clustering, requiring an effective method for calculating time series cluster centroids. This paper introduces Error in Aligned series (ERAL), a novel method that generates a time series centroid that distills the fundamental shape of the underlying datasets. ERAL employs a fuzzy clustering-inspired iterative process for temporal alignment and averaging, avoiding the pathological artifacts often introduced by popular time-warping methods. Our analysis shows that existing methods can create artificial spikes and plateaus, which ERAL mitigates; it remains faithful to the original data shape. Additionally, ERAL offers improvements in computational efficiency and prototype quality. We evaluate the method against Dynamic Time Warping (DTW)-based methods across various datasets, and apply it to a time series clustering task using a real-world industrial dataset. In combination with a fuzzy clustering algorithm, ERAL generates visually convincing clusters. By leveraging fuzzy membership concepts, it achieves robust and adaptable clustering outcomes that reflect real-world data complexity and ambiguity.

Study of tropical cyclone wave characteristics based on a hybrid track clustering method

Understanding the activity and wave characteristics of different TC track types with different formation mechanisms is crucial for coastal and ocean engineering. In this study, a hybrid clustering method based on track indicators representing track shape and influence region is proposed to classify 721 TC tracks along the coast of China into three track types. Due to the intrinsic similarity of track indicators diagnosed by the self-organizing map method, the initial clustering centroid selection of K-means method is reasonable. 5000 clustering trials show that the clustering results are more robust than those of the traditional K-means method, and the average difference percentage decreases from 11.3% to 5.5%. By analyzing the robust clustering results, the activity characteristics of three TC types under the background of atmospheric circulation patterns and the characteristics of the waves generated by the three TC types are studied, especially the characteristics of extreme waves. The joint distribution pattern of extreme wave height and wave direction indicates the direction sector shift and frequency change caused by different TC types. These results provide important insights for risk analysis and resilience construction of coastal and ocean engineering structures.

APPLICATION OF K-MEANS++ WITH DUNN INDEX VALIDATION OF GROUPING WEST KALIMANTAN REGION BASED ON CRIME VULNERABILITY

Crime is an unlawful behavior that will be given a punishment or sanctions based on Kitab Undang-Undang Hukum Pidana (KUHP) or other regulations in Indonesia. One of the provinces in Indonesia, namely West Kalimantan reported that criminal cases are increasing in 2021 and 2022. One of the solutions to minimize that case is grouping the district and city in West Kalimantan based on the level of vulnerability so the authority can be more responsive in solving these problems. The grouping can be done by cluster analysis. This analysis aims to group some objects based on the similarity of characteristics. K-Means++ is one of the methods of cluster analysis. K-Means++ is the development of K-Means, in which K-Means++ is smarter than K-Means in selecting the initial centroid because only one initial centroid is chosen randomly, and the initial centroids of the other clusters are done through calculations. This research uses secondary data from BPS of West Kalimantan, consisting of 10 variables. This research aims to form clusters to determine the level of vulnerability of each district and city in West Kalimantan. The selection of the optimal cluster is done by evaluating the cluster. One of these evaluations is the Dunn Index. Based on the analysis results, the optimum number of clusters is with a Dunn Index value of 0.55. The first cluster is categorized as non-vulnerable with ten members, the second cluster as vulnerable with three members, and the third cluster as very vulnerable with one member.

12500 Objective Evaluation Of Clustering Methods For Resolving The Heterogeneity Of PCOS

Abstract Disclosure: K. Brewer: None. R. Sisk: None. M. Dapas: None. C. Li: None. A. Dunaif: Consulting Fee; Self; AcaciaBio, Inc, Neurocine Biosciences, Inc. Speaker; Self; Quest Diagnostics. Other; Self; Co-Editor Endocrine Today, Healio, Slack Inc. Clustering methods have been used to resolve heterogeneity within complex diseases to elucidate underlying biologic mechanisms. We (Dapas et al. PLoS Med, 2020) and others have applied these methods to PCOS and identified discrete subtypes, including those that capture distinct reproductive or metabolic features. We objectively compared two widely used methods, hierarchical clustering (HC), which recursively merges subjects based on their similarity, and k-means (Km), which iteratively groups individuals to minimize their distance to cluster centroids. The number of clusters (k) was predefined in both approaches. We compared HC vs Km in 874 European ancestry PCOS cases diagnosed by NIH criteria using 8 traits (BMI, testosterone, SHBG, DHEAS, LH, FSH, fasting insulin, fasting glucose) and 9 traits with the addition of AMH. The ConsensusClusterPlus R package was used to evaluate cluster stability of both approaches with k=2, 3, or 4, using 8 or 9 traits. Genomewide association study (GWAS) meta-analysis was performed as reported with a discovery cohort (620 cases, 2951 controls) and a replication cohort (371 cases, 926 controls), except that genotypes were imputed to the newer TOPMED (r2) panel. Clustering using 8 traits and k=3 resulted in the best cluster stability for both HC and Km (pairwise consensus proportion: HC 0.98, Km 0.93) compared to k=2 with 8 traits (HC 0.88, Km 0.88) or 9 traits (HC 0.95, Km 0.92). No stability was observed for k=4 with 8 or 9 traits using either method (range: 0.35-0.66). Km showed slightly better cluster separation than HC (average silhouette width: Km 0.12 vs HC 0.09) with k=3 and 8 traits. The biologic relevance of the three PCOS subtypes, which we have designated as reproductive (cases with higher LH, FSH, SHBG), metabolic (higher BMI, insulin, glucose), and background, was assessed by GWAS, an orthogonal (i.e., uncorrelated) approach for subtype confirmation. The subtypes identified with HC had genomewide significant associations (metabolic subtype, c9orf3/FANCC, rs10761370, minor allele frequency [MAF] 0.47, p=1.21 x 10-8; background subtype, FSHB/ARL14EP, rs10835649, MAF 0.17, p=8.49 x 10-[1]0). There were no genomewide significant signals when the subtypes were identified with Km despite having a better cluster separation. In summary, only HC clusters appeared to capture biologically meaningful differences since two of the three subtypes thus identified were associated with genomewide significant loci. Neither the addition of AMH nor increasing the number of groups improved clustering metrics. Our results emphasize the importance of independent validation of clustering approaches using an orthogonal confirmation strategy such as GWAS. We conclude that HC clustering using 8 traits is superior to Km for resolving the genetic heterogeneity of PCOS. Presentation: 6/2/2024

12500 Objective Evaluation Of Clustering Methods For Resolving The Heterogeneity Of PCOS

Abstract Disclosure: K. Brewer: None. R. Sisk: None. M. Dapas: None. C. Li: None. A. Dunaif: Consulting Fee; Self; AcaciaBio, Inc, Neurocine Biosciences, Inc. Speaker; Self; Quest Diagnostics. Other; Self; Co-Editor Endocrine Today, Healio, Slack Inc. Clustering methods have been used to resolve heterogeneity within complex diseases to elucidate underlying biologic mechanisms. We (Dapas et al. PLoS Med, 2020) and others have applied these methods to PCOS and identified discrete subtypes, including those that capture distinct reproductive or metabolic features. We objectively compared two widely used methods, hierarchical clustering (HC), which recursively merges subjects based on their similarity, and k-means (Km), which iteratively groups individuals to minimize their distance to cluster centroids. The number of clusters (k) was predefined in both approaches. We compared HC vs Km in 874 European ancestry PCOS cases diagnosed by NIH criteria using 8 traits (BMI, testosterone, SHBG, DHEAS, LH, FSH, fasting insulin, fasting glucose) and 9 traits with the addition of AMH. The ConsensusClusterPlus R package was used to evaluate cluster stability of both approaches with k=2, 3, or 4, using 8 or 9 traits. Genomewide association study (GWAS) meta-analysis was performed as reported with a discovery cohort (620 cases, 2951 controls) and a replication cohort (371 cases, 926 controls), except that genotypes were imputed to the newer TOPMED (r2) panel. Clustering using 8 traits and k=3 resulted in the best cluster stability for both HC and Km (pairwise consensus proportion: HC 0.98, Km 0.93) compared to k=2 with 8 traits (HC 0.88, Km 0.88) or 9 traits (HC 0.95, Km 0.92). No stability was observed for k=4 with 8 or 9 traits using either method (range: 0.35-0.66). Km showed slightly better cluster separation than HC (average silhouette width: Km 0.12 vs HC 0.09) with k=3 and 8 traits. The biologic relevance of the three PCOS subtypes, which we have designated as reproductive (cases with higher LH, FSH, SHBG), metabolic (higher BMI, insulin, glucose), and background, was assessed by GWAS, an orthogonal (i.e., uncorrelated) approach for subtype confirmation. The subtypes identified with HC had genomewide significant associations (metabolic subtype, c9orf3/FANCC, rs10761370, minor allele frequency [MAF] 0.47, p=1.21 x 10-8; background subtype, FSHB/ARL14EP, rs10835649, MAF 0.17, p=8.49 x 10-[1]0). There were no genomewide significant signals when the subtypes were identified with Km despite having a better cluster separation. In summary, only HC clusters appeared to capture biologically meaningful differences since two of the three subtypes thus identified were associated with genomewide significant loci. Neither the addition of AMH nor increasing the number of groups improved clustering metrics. Our results emphasize the importance of independent validation of clustering approaches using an orthogonal confirmation strategy such as GWAS. We conclude that HC clustering using 8 traits is superior to Km for resolving the genetic heterogeneity of PCOS. Presentation: 6/2/2024

A Review of Convex Clustering From Multiple Perspectives: Models, Optimizations, Statistical Properties, Applications, and Connections.

Traditional partition-based clustering is very sensitive to the initialized centroids, which are easily stuck in the local minimum due to their nonconvex objectives. To this end, convex clustering is proposed by relaxing K -means clustering or hierarchical clustering. As an emerging and excellent clustering technology, convex clustering can solve the instability problems of partition-based clustering methods. Generally, convex clustering objective consists of the fidelity and the shrinkage terms. The fidelity term encourages the cluster centroids to estimate the observations and the shrinkage term shrinks the cluster centroids matrix so that their observations share the same cluster centroid in the same category. Regularized by the lpn -norm ( pn ∈ {1,2,+∞} ), the convex objective guarantees the global optimal solution of the cluster centroids. This survey conducts a comprehensive review of convex clustering. It starts with the convex clustering as well as its nonconvex variants and then concentrates on the optimization algorithms and the hyperparameters setting. In particular, the statistical properties, the applications, and the connections of convex clustering with other methods are reviewed and discussed thoroughly for a better understanding the convex clustering. Finally, we briefly summarize the development of convex clustering and present some potential directions for future research.

Clustering Anomalous Circulations and Their Implications on the Prediction of Minimum 2-m Air Temperature over Northeastern China during Winter

Abstract The ERA5 reanalysis during cold months (November–March) of 1979–2020 was used for determining four cluster centroids through the k-means for classifying regional anomalies of the daily geopotential height at 500 hPa (H500) over northeastern China. Empirical orthogonal function (EOF) was used to reduce dimensionality. Four clusters were linked to the EOF patterns with clear meteorological meanings, which are associated with the evolution of ridges and troughs over northeastern China. Those systems relate to warm and cold advections at 850 hPa. In each H500 cluster, the advection is the major contributor leading to temperature changes at 850 hPa, which significantly relates to the changes and anomalies of daily minimum air temperature at 2 m (T2min). Furthermore, the jet activities over Asia relate to more or less occurrence of specific H500 clusters in jet phases. This is because anomalous westerlies are generally in favor of positive anomalies of the vorticity tendency at 500 hPa. For the reforecasts during 2004–19 in the Chinese Meteorology Administration (CMA) S2S model, the hit rates above 50% for all the H500 clusters are within 9.5 days, which are in between those for the first two and the last two clusters. The correct prediction of H500 anomalies improves the T2min prediction up to 12 days, compared with 8 days for the incorrect one. The good prediction of the jet activities leads to a more accurate prediction of H500 anomalies. Therefore, improvement of the model prediction of jet activities and H500 anomalies will lead to better prediction of winter weather near the ground over northeastern China.

On the Comparative Utility of Entropic Learning versus Deep Learning for Long-Range ENSO Prediction

Abstract This paper compares the ability of deep learning and entropic learning methods to predict the probability of the Niño-3.4 index being above 0.4° (El Niño), below −0.4° (La Niña), or within both of these thresholds (neutral) at lead times of 3 up to 24 months. In particular, the performance, interpretability, and training cost of entropic learning methods, represented by the entropy-optimal scalable probabilistic approximation (eSPA) algorithm, are compared with deep learning methods, represented by a long short-term memory (LSTM) classifier, trained on the same dataset. Using only monthly data derived from a surface-forced ocean model over the period 1958–2018, the problem manifests as a canonical small-data challenge. Relative to the LSTM model, eSPA exhibits equivalent or better performance in terms of area under the receiver operating characteristic curve (AUC) for all lead times with a smaller computational cost by two and three orders of magnitude. Comparisons of AUC with other state-of-the-art deep learning models in the literature show that eSPA appears to also be more accurate than these models across all three classes. Composite images are generated for each of the cluster centroids from each trained eSPA model at each lead time. At shorter lead times, the composite images for the most significant clusters correspond to patterns representing mature or emerging/declining El Niño or La Niña states, while at longer lead times, they correspond to precursor states consisting of extratropical anomalies. Finally, modifications to the baseline dataset are explored, showing that improvements can be made in the parsimony of the trained eSPA model without sacrificing predictive power.

UAV clusters information geometry fusion positioning method with LEO satellite system

The existing Low-Earth-Orbit (LEO) positioning performance cannot meet the requirements of Unmanned Aerial Vehicle (UAV) clusters for high-precision real-time positioning in the Global Navigation Satellite System (GNSS) denial conditions. Therefore, this paper proposes a UAV Clusters Information Geometry Fusion Positioning (UC-IGFP) method using pseudo-ranges from the LEO satellites. A novel graph model for linking and computing between the UAV clusters and LEO satellites was established. By utilizing probability to describe the positional states of UAVs and sensor errors, the distributed multivariate Probability Fusion Cooperative Positioning (PF-CP) algorithm is proposed to achieve high-precision cooperative positioning and integration of the cluster. Criteria to select the centroid of the cluster were set. A new Kalman filter algorithm that is suitable for UAV clusters was designed based on the global benchmark and Riemann information geometry theory, which overcomes the discontinuity problem caused by the change of cluster centroids. Finally, the UC-IGFP method achieves the LEO continuous high-precision positioning of UAV clusters. The proposed method effectively addresses the positioning challenges caused by the strong direction of signal beams from LEO satellites and the insufficient constraint quantity of information sources at the edge nodes of the cluster. It significantly improves the accuracy and reliability of LEO-UAV cluster positioning. The results of comprehensive simulation experiments show that the proposed method has a 30.5% improvement in performance over the mainstream positioning methods, with a positioning error of 14.267 m.