Clustering Approach Research Articles

Research on six-joint industrial robotic arm positioning error compensation algorithm based on motion decomposition and improved CIWOA-BP neural network

Motion errors in the trajectory of a six-joint industrial robotic arm’s end-effector can significantly impact machining precision. Complex milling operations can lead to deviations from the intended path due to the robotic arm’s structural characteristics. These errors often exhibit periodic and position-dependent variations, underscoring the need for meticulous control measures. To address this challenge, we propose a novel motion decomposition-based error compensation technique for a six-joint industrial robotic arm. This approach involves breaking down the robot’s motion trajectory into distinct components and constructing prediction models for each component using a BP neural network. These models are then optimized using the Whale Optimization Algorithm (CIWOA) and an adaptive chaotic mapping clustering approach to improve efficiency and global optimization. The proposed method is applied to various motion types of the robotic arm, resulting in substantial enhancements in absolute positioning accuracy. Experimental validation confirms the reliability of the CIWOA-BP neural network prediction model and the effectiveness of the nonparametric accuracy compensation method in refining motion planning precision.

CHAI: consensus clustering through similarity matrix integration for cell-type identification.

Several methods have been developed to computationally predict cell-types for single cell RNA sequencing (scRNAseq) data. As methods are developed, a common problem for investigators has been identifying the best method they should apply to their specific use-case. To address this challenge, we present CHAI (consensus Clustering tHrough similArIty matrix integratIon for single cell-type identification), a wisdom of crowds approach for scRNAseq clustering. CHAI presents two competing methods which aggregate the clustering results from seven state-of-the-art clustering methods: CHAI-AvgSim and CHAI-SNF. CHAI-AvgSim and CHAI-SNF demonstrate superior performance across several benchmarking datasets. Furthermore, both CHAI methods outperform the most recent consensus clustering method, SAME-clustering. We demonstrate CHAI's practical use case by identifying a leader tumor cell cluster enriched with CDH3. CHAI provides a platform for multiomic integration, and we demonstrate CHAI-SNF to have improved performance when including spatial transcriptomics data. CHAI overcomes previous limitations by incorporating the most recent and top performing scRNAseq clustering algorithms into the aggregation framework. It is also an intuitive and easily customizable R package where users may add their own clustering methods to the pipeline, or down-select just the ones they want to use for the clustering aggregation. This ensures that as more advanced clustering algorithms are developed, CHAI will remain useful to the community as a generalized framework. CHAI is available as an open source R package on GitHub: https://github.com/lodimk2/chai.

Incomplete Multiview Clustering Based on Consensus Information.

In contrast to traditional single-view clustering methods, multiview clustering (MVC) approaches aim to extract, analyze, and integrate structural information from diverse perspectives, providing a more comprehensive understanding of internal data structures. However, with an increasing number of views, maintaining the integrity of view information becomes challenging, giving rise to incomplete MVC (IMVC) methods. While existing IMVC methods have shown notable performance on many incomplete multiview (IMV) databases, they still grapple with two key shortcomings: 1) they treat the information of each view as a whole, disregarding the differences among samples within each view; and 2) they rely on eigenvalue and eigenvector operations on the view matrix, limiting their scalability for large-scale samples and views. To overcome these limitations, we propose a novel multiview clustering with consistent information (IMVC-CI) of sample points. Our method explores the multiview information set of sample points to extract consensus structural information and subsequently restores unknown information in each view. Importantly, our approach operates independently on individual sample points, eliminating the need for eigenvalue and eigenvector operations on the view information matrix and facilitating parallel computation. This significantly enhances algorithmic efficiency and mitigates challenges associated with dimensionality. Experimental results on various public datasets demonstrate that our algorithm outperforms state-of-the-art IMVC methods in terms of clustering performance and computational efficiency. The code for our article has been uploaded to https://github.com/PhdJiayiTang/IMVC-CI.git.

Test-based patch clustering for automatically-generated patches assessment

Previous studies have shown that Automated Program Repair (apr) techniques suffer from the overfitting problem. Overfitting happens when a patch is run and the test suite does not reveal any error, but the patch actually does not fix the underlying bug or it introduces a new defect that is not covered by the test suite. Therefore, the patches generated by apr tools need to be validated by human programmers, which can be very costly, and prevents apr tool adoption in practice. Our work aims to minimize the number of plausible patches that programmers have to review, thereby reducing the time required to find a correct patch. We introduce a novel light-weight test-based patch clustering approach called xTestCluster, which clusters patches based on their dynamic behavior. xTestCluster is applied after the patch generation phase in order to analyze the generated patches from one or more repair tools and to provide more information about those patches for facilitating patch assessment. The novelty of xTestCluster lies in using information from execution of newly generated test cases to cluster patches generated by multiple APR approaches. A cluster is formed of patches that fail on the same generated test cases. The output from xTestCluster gives developers a) a way of reducing the number of patches to analyze, as they can focus on analyzing a sample of patches from each cluster, b) additional information (new test cases and their results) attached to each patch. After analyzing 902 plausible patches from 21 Java apr tools, our results show that xTestCluster is able to reduce the number of patches to review and analyze with a median of 50%. xTestCluster can save a significant amount of time for developers that have to review the multitude of patches generated by apr tools, and provides them with new test cases that expose the differences in behavior between generated patches. Moreover, xTestCluster can complement other patch assessment techniques that help detect patch misclassifications.

Ten year citation prediction model for systematic reviews using early years citation data

Citation counts are frequently used for assessing the scientific impact of articles. Current approaches for forecasting future citations counts have important limitations. This study aims to analyse and predict the trajectories of citation counts of systematic reviews (SR) based on their citation profiles in the previous years and predict quantiles of future citation counts. We included all SR published between 2010 and 2012 in medical journals indexed in the Web of Science. A longitudinal k-means (KML) clustering approach was applied to identify trajectories of citations counts 10 years after publication, according to the yearly citation count, the proportion of all cites attained in a specific year and the annual variation in citation counts. Finally, we built multinomial logistic regression models aiming to predict in what tercile or quartile of citation counts a SR would be 10 years after publication. Using clustering approaches, we obtained 24 groups of SR. Two groups (7.9% of the articles) had an average of > 200 citations, while two other groups (10.4% of the articles) presented an average of < 10 citations. The model predicting terciles of citation counts attained an accuracy of 72.8% (95%CI = 71.1–74.3%) and a kappa coefficient of 0.59 (95%CI = 0.57–0.62). Prediction of citation quartiles (combining the second and third quartiles into a single group) attained a accuracy of 76.2% (95%CI = 74.7–77.8%) and a kappa coefficient of 0.62 (95%CI = 0.59–0.64). This study provides an approach for predicting of future citations of SR based exclusively on citation counts from the previous years, with the models developed displaying an encouraging accuracy and agreement.

Levy flight based momentum search and Circle Rock Hyrax swarm optimization algorithms for energy effective clustering and optimal routing in wireless body area networks

In recent years, the connectivity of a new dimension has been brought to Wireless Body Area Networks (WBANs) through the concept of the Wearable Internet of Things (WIoT). WIoT offers valuable functionalities like data collection, analysis, and transmission for real-time health monitoring services. However, WBANs are characterized by a large number of battery-powered sensor nodes, making them resource-constrained. While keeping standard WSN abilities, WBAN's significant obstacles are data communication, social mobility, high interference in dense deployment, latency, and energy consumption of sensor nodes. As a result, the implementation and design of an application require dependable data transfer and an energy-efficient architecture. It proved critical to have as little delay as possible while delivering correct data. To overcome these issues, this study developed a novel energy-efficient clustered and optimized routing scheme in WBAN. Due to its effective scalability, this work employs an Improved Self-Executing Active Cross-Propagated (ISAC) clustering approach to form clusters in Wireless Body Area Networks (WBANs). Subsequently, the selection of the best Cluster Heads (CHs) is facilitated by the Levy Flight-based Momentum Search Algorithm (LFMSA). The Circle Rock Hyrax Swarm Optimization (CRH) algorithm is then utilized for energy-efficient routing. In determining the CH in each cluster, consideration is given to both residual energy and connectivity. The proposed framework minimizes the number of transmissions and overall transmission distance, optimizing inter and intra-cluster transmission distances. Additionally, the framework ensures energy-efficient packet delivery from CHs to the sink node. Performance comparisons, conducted using various statistical measures and simulated on the NS-2 platform, demonstrate that the proposed framework outperforms existing routing protocols and clustering methodologies. In comparison to previous methodologies, the proposed framework consistently achieves superior results.

Categorisation of urban open spaces for heat adaptation: A cluster based approach

In the context of climate change, adapting a city's open spaces to heat waves and extreme heat is crucial for mitigating the Urban Heat Island effect and ensuring the wellbeing of its inhabitants. However, the heat adaptation potential of open spaces varies within a city. This study develops an objective and replicable method to categorise urban areas based on their open spaces' adaptive capacity and improvement potential to heat waves at the micro-scale. This is achieved using a clustering approach, eliminating manual operations, using openly available data, employing indicators suitable for every urban fabric type, and using the urban block as the analysis unit. The validation of the method in the case study of Bilbao (Spain) demonstrates its capacity to obtain meaningful insights regarding open space heat adaptation potential. An analysis of the resulting categories reveals significant open space improvement potential throughout the city and a varying adaptive capacity that depends on urban density. Categories with the lowest adaptive capacity have median open space ratios between 0.35 and 0.65 and median tree cover ratios between 0.07 and 0.15. Those with the highest adaptive capacity present median open space ratios between 0.8 and 1 and median tree cover ratios between 0.05 and 0.25. The method can aid local policymakers in identifying opportunity spots for hosting adaptation solutions and understanding the challenges the city may face in planning adaptation action. The method's replicability enables it to be applied in other cities, contributing to a broader exploration of climate change adaptation potential.

An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based On Ensemble Weather Forecast Data

AbstractIn ensemble weather forecast, tropical cyclone (TC) tracks sometimes group together into trajectories parting away from each other. The goal of this study is to propose an objective method, based on a robust clustering approach, to detect such separation scenarios in the Japan Meteorological Agency Meso‐scale Ensemble Prediction System (MEPS) for three TCs: “Dolphin” (2020), “Nepartak” (2021), and “Meari” (2022). Taking advantage of the independence of the density‐based spatial clustering of applications with noise algorithm to the prior choice of the number of clusters, we first describe an objective way to calculate the aggregation distance, by searching the most frequent Euclidean distance between all the tracks. The clustering is then applied to the forecasted tracks, for each initialization time of MEPS (every 6 hr). Separation scenarios exist when the number of clusters is greater than one.

Fine-grained subphenotypes in acute kidney injury populations based on deep clustering: Derivation and interpretation

BackgroundAcute kidney injury (AKI) is associated with increased mortality in critically ill patients. Due to differences in the etiology and pathophysiological mechanism, the current AKI criteria put it an embarrassment to evaluate clinical therapy and prognosis. ObjectiveWe aimed to identify subphenotypes based on routinely collected clinical data to expose the unique pathophysiologic patterns. MethodsA retrospective study was conducted based on the Medical Information Mart for Intensive Care IV (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD), and a deep clustering approach was conducted to derive subphenotypes. We conducted further analysis to uncover the underlying clinical patterns and interpret the subphenotype derivation. ResultsWe studied 14,189 and 19,382 patients with AKI within 48 h of ICU admission in the two datasets, respectively. Through our approach, we identified seven distinct AKI subphenotypes with mortality heterogeneity in each cohort. These subphenotypes displayed significant variations in demographics, comorbidities, levels of laboratory measurements, and survival patterns. Notably, the subphenotypes could not be effectively characterized using the Kidney Disease: Improving Global Outcomes (KDIGO) criteria alone. Therefore, we uncovered the unique underlying characteristics of each subphenotype through model-based interpretation. To assess the usability of the subphenotypes, we conducted an evaluation, which yielded a micro-Area Under the Receiver Operating Characteristic (AUROC) of 0.81 in the single-center cohort and 0.83 in the multi-center cohort within 48-hour of admission. ConclusionWe derived highly characteristic, interpretable, and usable AKI subphenotypes that exhibited superior prognostic values.

Deep Image: A precious image based deep learning method for online malware detection in IoT environment

In this study, we address the challenge of online malware detection for IoT devices. We propose a method that monitors malware behavior, extracts dynamic features, and converts them into sparse binary images for analysis. The primary problem is to identify the most effective approach among clustering, probabilistic, and deep learning methods for analyzing this unique image dataset. We extract dynamic features from the monitored malware behavior, transforming them into binary images, which are then subjected to three different analysis methods. The clustering, probabilistic, and deep learning approaches are compared and evaluated in terms of various metrics. Our study contributes insights into the performance of various online malware detection approaches for IoT devices. We demonstrate that deep learning outperforms other methods, achieving the best results in seven out of eight metrics. The results of our analysis reveal that the deep learning approach exhibits the highest accuracy in seven of the eight evaluated metrics. We found that the lattice-based approach consistently returns the maximum maliciousness level, which can be instrumental in label flipping scenarios.

An Energy-Balance Clustering Routing Protocol for Intra-Body Wireless Nanosensor Networks

Aims and Background: Numerous sensor nodes spread out across the surveillance region form the Wireless-Sensor Network (WSN), a smart, self-organizing network. Since the lumps can typically only be motorized by batteries, creating a WSN while maintaining an optimal energy balance and extending the network's lifetime is the biggest issue. Methods: A novel network architecture that integrates nanotechnology with sensor networks is known as a Wireless-NanoSensor-Network(WNSN). A new area of focus in research is intra-bodyiWNSNs, which are WNSNs with promising potential applications in biomedicine, damage detection, and intra-body health monitoring. We suggest an energy-balance-clustering-routing protocol (EBCR) for iSN nodes that have limited energy storage, short communication range, and low computation and processing capabilities. The protocol uses a novel hierarchical clustering approach to lessen the communication burden on nano-nodes. Results: Cluster nano-nodes can use one-hop routing to send data directly to the Cluster-Head(CH) nodes, and the CH-nodes can utilize multi-hop routing to send data to the nano control node. In addition, selecting the next hop node to minimize energy usage while guaranteeing successful data packet delivery involves balancing distance and channel capacity. The protocol's strengths in energy efficiency, network-lifetime extension, and data-packet transmission success rate were highlighted by the simulation results. Conclusion: It is clear that the EBCR protocol is a viable option for iWNSNs' routing system.

Minimum spanning tree clustering approach for effective feature partitioning in multi-view ensemble learning

Minimum spanning tree clustering approach for effective feature partitioning in multi-view ensemble learning

Green Infrastructure Vulnerability and Regional Poverty Reduction: New Sustainable Development Recommendations Based on a Spatial Clustering Approach

ABSTRACT Poverty is always associated with poor green infrastructure. Indonesia has a significant poverty rate and low green infrastructure investment. Therefore, this study uses fuzzy clustering with area weighting (FGWC-HHOP) and 2018 Potential Village Census (PODES) data to analyze green infrastructure risk and poverty in Indonesia. The first cluster, which is mainly urban, is vulnerable, while the second cluster has poor air quality and green infrastructure. Future government policies should encourage sustainable development, especially green infrastructure, to reduce social vulnerability. Consider green infrastructure and social vulnerability when planning urban expansion and poverty alleviation in Indonesia.

Municipal street‐sweeping area generation with route optimization

AbstractStreet sweeping is used in urban municipalities worldwide but requires a great deal of planning for large‐scale implementation. Municipalities typically make use of the macro‐ and microapproach by using operational areas in which routes are assigned, but creating operational areas without an understanding of the expected workload, number of routes, and the travel distance to and from the depot may lead to increased statistics. In this paper, a combination of heuristic approaches is used to assign street‐sweeping areas on the macroscale and generate street‐sweeping routes on the microscale. For the area assignment, a two‐stage cluster approach is proposed, making use of the weighted k‐means algorithm and differential evolution. For the route optimization, a three‐phase augment merge algorithm is used to create initial solutions, the u‐turns are minimized with a modified version of Hierholzer's algorithm and tabu search, and the remaining u‐turns are removed using a forward‐searching ant colony optimization. A case study in The City of Oshawa, Canada, was used to verify the proposed methodology, and all metrics were theoretically improved.

Classroom profiles of instructional quality: contribution of level and variability of students’ perception

Different approaches for measuring instructional quality have been debated. Previous studies based on student ratings have primarily used class-average ratings. Beyond this, the high within-classroom variability of students’ ratings might indicate that instruction caters to some, but not all students. Therefore, we investigated student-reported instructional quality in mathematics classrooms by considering the average student ratings and rating heterogeneity within classrooms. Using a case-centered clustering approach, we aimed to detect meaningful configurations of the level and heterogeneity of student-reported instructional quality in terms of the Three Basic Dimensions (TBD): classroom management, cognitive activation, and student support. We analyzed data from N = 973 grade eight students across N = 106 classes. Using Latent Profile Analysis (LPA), we identified four classroom profiles comprising 20% to 28% of the sample. The results indicate that the profile with the lowest average ratings showed consistently high heterogeneity for all indicator variables. However, the profile with the highest average ratings exhibited consistently low heterogeneity. We gained interesting insights into between-class differences in instructional quality by considering rating heterogeneity. Furthermore, we explored how classrooms from the identified profiles differed regarding socio-economic status (SES) and mathematics-related characteristics (interest, intrinsic motivation, self-concept, and achievement).

Methodological approaches to the assessment of balanced spatial development of regions

Subject. This article discusses the problem of uneven development of regions. Objectives. The article aims to analyze the methods of smoothing spatial polarization and develop an algorithm for assessing the balance of regional development. Methods. For the study, we used a comparative analysis, and the tabular and graphic visualization techniques. Results. The article finds that the processes of polarization are due to socio-economic inequality, and it presents an original methodology for assessing the balance of spatial development, developed on the basis of the cluster approach. Conclusions. At the State level, it is necessary to approve a unified methodology for assessing the balance of Russia's spatial development. When developing programmes for the development of regions, it is necessary to be guided by the concept of leveling spatial imbalances.

Scalable Image Clustering to screen for self-produced CSAM

The number of cases involving Child Sexual Abuse Material (CSAM) has increased dramatically in recent years, resulting in significant backlogs. To protect children in the suspect’s sphere of influence, immediate identification of self-produced CSAM among acquired CSAM is paramount. Currently, investigators often rely on an approach based on a simple metadata search. However, this approach faces scalability limitations for large cases and is ineffective against anti-forensic measures. Therefore, to address these problems, we bridge the gap between digital forensics and state-of-the-art data science clustering approaches. Our approach enables clustering of more than 130,000 images, which is eight times larger than previous achievements, using commodity hardware and within an hour with the ability to scale even further. In addition, we evaluate the effectiveness of our approach on seven publicly available forensic image databases, taking into account factors such as anti-forensic measures and social media post-processing. Our results show an excellent median clustering-precision (Homogeinity) of 0.92 on native images and a median clustering-recall (Completeness) of over 0.92 for each test set. Importantly, we provide full reproducibility using only publicly available algorithms, implementations, and image databases.

Understanding non‐stationarity patterns in basin‐scale hydroclimatic extremes

AbstractStationarity, a cornerstone in hydraulic design, is now under scrutiny due to anthropogenic activities and climate change. Numerous studies have sought to identify non‐stationarity (NS); however, a comprehensive assessment of time invariance in all statistical properties of a time series is less explored. This study presents a non‐overlapping block‐stratified random sampling (NBRS) framework leveraging the strengths of several nonparametric tests to assess NS. The NBRS approach exclusively detects NS and distinguishes between various forms of stationarity, including weak and strict. A variant of NBRS is proposed in this study to identify the underlying stochastic process(es) influencing NS in hydroclimatic extremes. Furthermore, a nonparametric clustering approach is used to unveil spatial clusters showcasing NS due to shifts in mean, variance, distribution of time series or a combination of these factors. A comparative assessment of the modified NBRS approach with traditional trend and change point methods is also performed. The proposed methodology is applied to assess the presence of NS in 28 hydroclimatic indices derived for the west‐central river basins of India, exhibiting diverse physio‐climatic settings, for the study period 1973–2021. The modified NBRS approach rigorously explores NS within extreme hydroclimatic indices, conclusively pinpointing its root causes and profound implications for hydrologic design. The applicability of the modified NBRS approach to gridded and point datasets is also demonstrated. The findings highlight the limitations of conventional trend and change point tests in capturing time‐invariant characteristics in heteroscedastic variables (such as streamflow and rainfall extremes) compared to the NBRS approach. The research reveals that NS in rainfall and streamflow extremes primarily results from distributional shifts, whilst temperature extremes are influenced by changes in mean and distribution properties. This research deepens our understanding of the evolving patterns in hydroclimatic extremes in a changing climate.

A Comparison between several Density-Based Anomaly Detection Approaches

The current world of internet, mobile devices, businesses, social media platforms, healthcare systems, and the Internet of Things all have a lot of data available online. The enormous volume of data, dimensionality, and dataset changes throughout time are these problems. Clustering algorithms are a useful tool for solving this type of problem. Consequently, the first step in resolving these issues is the application of clustering algorithms, which are necessary for data mining procedures to reveal the structure and hidden patterns in given datasets. Four clustering algorithms OPTICS (Ordering Points To Identify Clustering Structure), Hierarchical Clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), AGGLOMERATIVE and HDBSCAN. The efficiency of each clustering approach is assessed using a range of external and internal parametric clustering assessment metrics.

Flexible density peak clustering for real-world data

In density based clustering, the density peak algorithm has attracted much attention due to its effectiveness and simplicity, and a vast amount of clustering approaches have been proposed based on this algorithm. Some of these works require manual selection of cluster centers with a decision graph, where human involvement leads to uncertainty in clustering results. In order to avoid human involvement, some other algorithms depend on user-specified number of clusters to determine cluster centers automatically. However, it is well known that accurate estimation of number of clusters is a long-standing difficulty in data clustering. In this paper we present a sequential density peak clustering algorithm to extract clusters one by one, thereby determining the number of clusters automatically and avoiding manual selection of cluster centers in the meanwhile. Starting from a density peak, our algorithm generates an initial cluster surrounding the density peak in the first step, and then obtains the final cluster by expanding the initial cluster based on the relative density relationship among neighboring data points. With a peeling-off strategy, we obtain all the clusters sequentially. Our algorithm works well with clusters of Gaussian distribution and is therefore potential for clustering of real-world data. Experiments with a large number of synthetic and real datasets and comparisons with existing algorithms demonstrate the effectiveness of the proposed algorithm.