Clustering Algorithm Research Articles

Method for constructing typical operation scenarios in power systems considering spatiotemporal correlation of renewable energy generation

Abstract The proposed article focuses on developing a methodology for constructing typical operational scenarios in power systems considering the spatiotemporal characteristics of new energy output. The integration of new energy sources on a large scale into power systems can lead to issues such as line congestion and supply-demand imbalances. To support the planning and scheduling of future high-capacity power systems, this paper introduces a method that accounts for the spatiotemporal characteristics of new energy outputs. The method begins by proposing a spatiotemporal fusion-based multi-head attention model for generating new energy output scenarios. This model employs multi-head attention mechanisms to extract the spatiotemporal characteristics of new energy outputs, eliminating redundancy in the feature data. The processed spatiotemporal feature information is then transferred to a gated feature fusion model for integration, using an encoder structure based on Long Short-Term Memory (LSTM) to generate new energy output scenarios. The paper, by means of an advanced Fuzzy C-Means (FCM) clustering algorithm, identifies typical scenarios of net energy output. Subsequently, the proposed method’s efficacy and precision are confirmed through simulation analysis with real data from a certain area.

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

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

Research on transmission line component detection based on scenario prior knowledge and knowledge distillation

Abstract Transmission lines for power systems are often located outdoors, which exposes them to various hazards that can seriously impact system reliability. Integrating UAV inspection and intelligent detection technology has emerged as a primary method for operational and maintenance tasks. However, challenges persist regarding detection accuracy and speed. This paper introduces a novel transmission line component detection approach, leveraging scene prior knowledge and knowledge distillation techniques. Initially, high-resolution aerial images of transmission lines are perceptually analysed. Subsequently, scene prior knowledge informs the selection of clustering algorithm parameters, enabling adaptive sub-region clustering and model optimization. Knowledge distillation is then applied to transfer comprehensive image feature knowledge from a teacher model to a student network, utilizing the obtained sub-regions as shared feature distillation masks. The efficacy of the proposed method is evaluated through extensive dataset analysis, demonstrating significant accuracy improvements ranging from 2.6% to 49.0% compared to alternative models.

Mahalanobis-Kernel Distance-Based Suppressed Possibilistic C-Means Clustering Algorithm for Imbalanced Image Segmentation

Mahalanobis-Kernel Distance-Based Suppressed Possibilistic C-Means Clustering Algorithm for Imbalanced Image Segmentation

Supervertex Sampling Network: A Geodesic Differential SLIC Approach for 3D Mesh.

The analysis of 3D meshes with deep learning has become prevalent in computer graphics. As an essential structure, hierarchical representation is critical for mesh pooling in multiscale analysis. Existing clustering-based mesh hierarchy construction methods involve nonlinear discretization optimization operations, making them nondifferential and challenging to embed in other trainable networks for learning. Inspired by deep superpixel learning methods in image processing, we extend them from 2D images to 3D meshes by proposing a novel differentiable chart-based segmentation method named geodesic differential supervertex (GDSV). The key to the GDSV method is to ensure that the geodesic position updates are differentiable while satisfying the constraint that the renewed supervertices lie on the manifold surface. To this end, in addition to using the differential SLIC clustering algorithm to update the nonpositional features of the supervertices, a reparameterization trick, the Gumbel-Softmax trick, is employed to renew the geodesic positions of the supervertices. Therefore, the geodesic position update problem is converted into a linear matrix multiplication issue. The GDSV method can be an independent module for chart-based segmentation tasks. Meanwhile, it can be combined with the front-end feature learning network and the back-end task-specific network as a plug-in-plug-out module for training; and be applied to tasks such as shape classification, part segmentation, and 3D scene understanding. Experimental results show the excellent performance of our proposed algorithm on a range of datasets.

Data Mining on Women's Clothing Sales in Market Places with the K-Means Clustering Algorithm

Clothing is a necessity that must be used to cover the body with the main material made of fiber or textile so that the body is completely covered without gaps. Marketplace is an application or website that provides online buying and selling facilities from various sources. On the Shopee marketplace, there are many shops selling women's clothing from various groups and types of clothing. The K-Means Clustering algorithm in the research was applied to make it easier for sellers and buyers to find out what kind of women's clothing is currently selling well in the marketplace by grouping it into 3 clusters, namely the best-selling, best-selling, and least-selling. Research data was obtained from the Shopee marketplace with 3 variables, namely product price, number of sales, and buyer assessments of 4 types of women's clothing in the form of tunics, dresses, abayas/gamis, and shirts totaling 1200 data. The results of this research make it easier for buyers to make decisions and sellers to develop shop ideas.

CBCG: A Clustering Algorithm Based on Bidirectional Conical Information Granularity

CBCG: A Clustering Algorithm Based on Bidirectional Conical Information Granularity

Development of Clustering Algorithm for Pixel Detectors for FPGA

Development of Clustering Algorithm for Pixel Detectors for FPGA

Abnormal large-scale brain functional network dynamics in social anxiety disorder.

Although static abnormalities of functional brain networks have been observed in patients with social anxiety disorder (SAD), the brain connectome dynamics at the macroscale network level remain obscure. We therefore used a multivariate data-driven method to search for dynamic functional network connectivity (dFNC) alterations in SAD. We conducted spatial independent component analysis, and used a sliding-window approach with a k-means clustering algorithm, to characterize the recurring states of brain resting-state networks; then state transition metrics and FNC strength in the different states were compared between SAD patients and healthy controls (HC), and the relationship to SAD clinical characteristics was explored. Four distinct recurring states were identified. Compared with HC, SAD patients demonstrated higher fractional windows and mean dwelling time in the highest-frequency State 3, representing "widely weaker" FNC, but lower in States 2 and 4, representing "locally stronger" and "widely stronger" FNC, respectively. In State 1, representing "widely moderate" FNC, SAD patients showed decreased FNC mainly between the default mode network and the attention and perceptual networks. Some aberrant dFNC signatures correlated with illness duration. These aberrant patterns of brain functional synchronization dynamics among large-scale resting-state networks may provide new insights into the neuro-functional underpinnings of SAD.

Fuzz C-Means Clustering Algorithm for Hybrid TOA and AOA Localization in NLOS Environments

Fuzz C-Means Clustering Algorithm for Hybrid TOA and AOA Localization in NLOS Environments

Generation of input spectrum for electrolysis stack degradation test applied to wind power PEM hydrogen production

Hydrogen production by proton exchange membrane electrolysis has good fluctuation adaptability, making it suitable for hydrogen production by electrolysis in fluctuating power sources such as wind power. However, current research on the durability of proton exchange membrane electrolyzers is insufficient. Studying the typical operating conditions of wind power electrolysis for hydrogen production can provide boundary conditions for performance and degradation tests of electrolysis stacks. In this study, the operating condition spectrum of an electrolysis stack degradation test cycle was proposed. Based on the rate of change of the wind farm output power and the time-averaged peak-valley difference, a fluctuation output power sample set was formed. The characteristic quantities that played an important role in the degradation of the electrolysis stack were selected. Dimensionality reduction of the operating data was performed using principal component analysis. Clustering analysis of the data segments was completed using an improved Gaussian mixture clustering algorithm. Taking the annual output power data of wind farms in Northwest China with a sampling rate of 1 min as an example, the cyclic operating condition spectrum of the proton-exchange membrane electrolysis stack degradation test was constructed. After preliminary simulation analysis, the typical operating condition proposed in this paper effectively reflects the impact of the original curve on the performance degradation of the electrolysis stack. This study provides a method for evaluating the degradation characteristics and system efficiency of an electrolysis stack due to fluctuations in renewable energy.

The artificial intelligence advantage: Supercharging exploratory data analysis.

Explorative data analysis (EDA) is a critical step in scientific projects, aiming to uncover valuable insights and patterns within data. Traditionally, EDA involves manual inspection, visualization, and various statistical methods. The advent of artificial intelligence (AI) and machine learning (ML) has the potential to improve EDA, offering more sophisticated approaches that enhance its efficacy. This review explores how AI and ML algorithms can improve feature engineering and selection during EDA, leading to more robust predictive models and data-driven decisions. Tree-based models, regularized regression, and clustering algorithms were identified as key techniques. These methods automate feature importance ranking, handle complex interactions, perform feature selection, reveal hidden groupings, and detect anomalies. Real-world applications include risk prediction in total hip arthroplasty and subgroup identification in scoliosis patients. Recent advances in explainable AI and EDA automation show potential for further improvement. The integration of AI and ML into EDA accelerates tasks and uncovers sophisticated insights. However, effective utilization requires a deep understanding of the algorithms, their assumptions, and limitations, along with domain knowledge for proper interpretation. As data continues to grow, AI will play an increasingly pivotal role in EDA when combined with human expertise, driving more informed, data-driven decision-making across various scientific domains. Level of Evidence: Level V - Expert opinion.

Unraveling the propagation dynamics of passenger congestion in public transportation systems using big smart card data

We propose a comprehensive framework to explore propagation of passenger crowdedness in public transportation systems using 74.16 million trip chains. A deep learning method HSTGCNT is adopted which firstly models normal spatio-temporal dependencies in time series data, and assigns an anomaly score to new data based on its prediction error. The derived discrete anomaly events are further aggregated into groups based on their spatio-temporal proximity by using a clustering algorithm that binds shared nearest neighbors together based on pairwise correlations. The clustering results uncover several key stages located in a propagation chain, such as a core event, adjacent events with spatio-temporal proximity, or a broadcasting group. Weibo data serves as a third-party dataset to verify the above anomaly clusters. Results show that the framework is effective to detect outlier clusters, with recall value being 0.86. Findings may assist public safety departments in taking pro-active actions to interrupt chaos from broadcasting.

Railway alignment optimization in regions with densely-distributed obstacles based on semantic topological maps

Railway alignment development in a study area with densely-distributed obstacles, in which regions favorable for alignments are isolated (termed an isolated island effect, i.e., IIE), is a computation-intensive and time-consuming task. To enhance search efficiency and solution quality, an environmental suitability analysis is conducted to identify alignment-favorable regions (AFRs), focusing the subsequent alignment search on these areas. Firstly, a density-based clustering algorithm (DBSCAN) and a specific criterion are customized to distinguish AFR distribution patterns: continuously-distributed AFRs, obstructed effects, and IIEs. Secondly, a study area characterized by IIEs is represented with a semantic topological map (STM), integrating between-island and within-island paths. Specifically, between-island paths are derived through a multi-directional scanning strategy, while within-island paths are optimized using a Floyd-Warshall algorithm. To this end, the intricate alignment optimization problem is simplified into a shortest path problem, tackled with conventional shortest path algorithms (of which Dijkstra’s algorithm is adopted in this work). Lastly, the proposed method is applied to a real case in a mountainous region with karst landforms. Numerical results indicate its superior performance in both construction costs and environmental suitability compared to human designers and a prior alignment optimization method.

Toward an Efficient Hybrid Clustering and Classification Approach for Fake News Detection in Social Network

Objectives: The spread of fake news on social media has become a pressing issue in recent times. Despite various organizations' efforts to address this problem, it continues to persist, necessitating finding more effective solutions. This study implements a machine learning-based approach for identifying fake news on social media with improved accuracy. Methods : The study's methodology utilizes a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to extract semantic features from news articles. Then, a hybrid clustering and classification approach is used, which combines K-means clustering and Artificial Neural Network (ANN) classifiers. Data used is a secondary data consisting of 23481 world news articles obtained from the Reuters.com website and 21417 unreliable news from polifact.com. During the training process, the number of units and layers on the model was tuned to optimize model performance. The model was compared to other baseline models such as KNN, SVM, Decision tree, and Boosted decision tree to establish the best performing model. Findings: The results from the two algorithms were weighted to final classifications using Bayesian probability theory. The proposed approach achieved an accuracy of 99.78%, a sensitivity of 100%, and specificity equal to 99.73%. The model's precision is 99.74%, indicating its ability to identify fake news. The F-score of the approach is 99.87%, indicating that the model strikes a good balance between correctly classifying fake news articles and reliable news articles. The approach outperformed other machine learning classifiers, including KNN, SVM, Decision Tree, and Boosted Decision Tree. Novelty : The study applies a hybrid approach with a classification and clustering algorithm to improve detection of fake news on social media, the approach is tested with varied real-world datasets to establish its robustness under different vocabularies and vocabulary sizes. Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Hybrid clustering approach, Classification, CNN, LSTM, Boosted Decision Tree, Social platforms, K-means

Implementation of the K-Means Clustering Method in Clustering Poor Population in Bandar Kumbul Village, Labuhanbatu Regency

Poverty is one of the crucial social problems in rural areas. The problem of poverty in rural areas is increasingly in the spotlight because of its broad impact on the community's economic and social sustainability. The varying levels of poverty require an appropriate analytical approach to design effective intervention programs. In an effort to understand and address this problem, this study uses the K-Means Clustering method to group the poor population. We use K-Means clustering to identify and group hamlets based on their poverty levels. This study aims to categorize the hamlets in Bandar Kumbul Village into multiple clusters according to their poverty levels, thereby identifying which hamlets necessitate more focused attention. The research methods used include collecting data on the number of poor people from 2013 to 2022 in each hamlet, data preprocessing, applying the Elbow method to determine the optimal number of clusters, and applying the K-Means Clustering algorithm to group the hamlets. The results of the study show that there are three main clusters with different characteristics. Cluster 0 includes Hutaimbaru and Mailil Julu hamlets with high poverty levels. Cluster 1 only includes the Pasir Sidimpuan hamlet, which has medium poverty levels. Cluster 2 includes Aek Mardomu, Bandar Kumbul, Mailil Jae, Sidodadi, and Singga Mata hamlets with low poverty levels. Variations in distance from the cluster center indicate significant differences in the distribution of poverty in each hamlet. The K-Means Clustering method is effective in identifying and grouping hamlets based on poverty levels, providing useful insights for the government and stakeholders to design more targeted intervention programs. Clusters with high poverty levels require immediate intervention, while clusters with medium and low poverty levels require maintenance and support to prevent an increase in poverty. This study provides a strong foundation for decision-making and policies to reduce poverty levels in Bandar Kumbul Village more effectively and sustainably.

Tracking Census Online Self-Completion Using Twitter Posts

From the start of data collection for the 2020 US Census, official and celebrity users tweeted about the importance of everyone being counted in the Census and urged followers to complete the questionnaire (so-called social media campaign.) At the same time, social media posts expressing skepticism about the Census became increasingly common. This study distinguishes between different prototypical Twitter user groups and investigates their possible impact on (online) self-completion rate for the 2020 Census, according to Census Bureau data. Using a network analysis method, Community Detection, and a clustering algorithm, Latent Dirichlet Allocation (LDA), three prototypical user groups were identified: “Official Government Agency,” “Census Advocate,” and “Census Skeptic.” The prototypical Census Skeptic user was motivated by events about which an influential person had tweeted (e.g., “Republicans in Congress signal Census cannot take extra time to count”). This group became the largest one over the study period. The prototypical Census Advocate was motivated more by official tweets and was more active than the prototypical Census Skeptic. The Official Government Agency user group was the smallest of the three, but their messages—primarily promoting completion of the Census—seemed to have been amplified by Census Advocate, especially celebrities and politicians. We found that the daily size of the Census Advocate user group—but not the other two—predicted the 2020 Census online self-completion rate within five days after a tweet was posted. This finding suggests that the Census social media campaign was successful in promoting completion, apparently due to the help of Census Advocate users who encouraged people to fill out the Census and amplified official tweets. This finding demonstrates that a social media campaign can positively affect public behavior regarding an essential national project like the Decennial Census.

Terraced compression method with automated threshold selection using GMM algorithm for heterogeneous bodies detection

Breast tumors threaten women’s health. Multispectral transmission imaging shows potential in early breast cancer screening, but images have a low signal-to-noise ratio due to tissue absorption and scattering. Frame accumulation improves quality but leads to data redundancy. Existing terraced compression methods (TCM) address this but rely on subjective, inefficient threshold selection. This paper presents an improved TCM that requires only the input of the desired heterogeneity size, with optimal thresholds adaptively determined by analyzing grayscale values and their distribution. By applying compression to each wavelength image and using the GMM algorithm for multidimensional image clustering, results show the Dice coefficient improved by 12.49 %, and the recognition rate of deeper heterogeneity increased by 29.09 % compared to the existing TCM. This validates the TCM’s effectiveness and threshold selection accuracy. Additionally, the method distinguishes heterogeneous bodies of different depths and effectively recognizes various kinds, providing a reliable reference for early breast cancer screening.

Research on a Train Safety Driving Method Based on Fusion of an Incremental Clustering Algorithm and Lightweight Shared Convolution.

This paper addresses the challenge of detecting unknown or unforeseen obstacles in railway track transportation, proposing an innovative detection strategy that integrates an incremental clustering algorithm with lightweight segmentation techniques. In the detection phase, the paper innovatively employs the incremental clustering algorithm as a core method, combined with dilation and erosion theories, to expand the boundaries of point cloud clusters, merging adjacent point cloud elements into unified clusters. This method effectively identifies and connects spatially adjacent point cloud clusters while efficiently eliminating noise from target object point clouds, thereby achieving more precise recognition of unknown obstacles on the track. Furthermore, the effective integration of this algorithm with lightweight shared convolutional semantic segmentation algorithms enables accurate localization of obstacles. Experimental results using two combined public datasets demonstrate that the obstacle detection average recall rate of the proposed method reaches 90.3%, significantly enhancing system reliability. These findings indicate that the proposed detection strategy effectively improves the accuracy and real-time performance of obstacle recognition, thereby presenting important practical application value for ensuring the safe operation of railway tracks.

A dynamic customer segmentation approach by combining LRFMS and multivariate time series clustering

To successfully market to automotive parts customers in the Industrial Internet era, parts agents need to perform effective customer analysis and management. Dynamic customer segmentation is an effective analytical tool that helps parts agents identify different customer groups. RFM model and time series clustering algorithms are commonly used analytical methods in dynamic customer segmentation. The original RFM model suffers from the problems of R index randomness and ignoring customers’ perceived value. For most existing studies on dynamic customer segmentation, time series clustering techniques largely focus on univariate clustering, with less research on multivariate clustering. To solve the above problems, this paper proposes a dynamic customer segmentation approach by combining LRFMS and multivariate time series clustering. Firstly, this method represents each customer behavior as a time series sequence of the Length, Recency, Frequency, Monetary and Satisfaction variables. And then, we apply a multi-dimensional time series clustering algorithm based on three distance measurement methods called DTW-D, SBD, and CID to carry out customer segmentation. Finally, an empirical study and comparative analyses are conducted using customer transaction data of parts agents to verify the effectiveness of the approach. Additionally, a detailed analysis of different customer groups is made, and corresponding marketing suggestions are provided.