C-means Clustering Algorithm Research Articles

Modal Parameter Identification of Electric Spindles Based on Covariance-Driven Stochastic Subspace

Electric spindles are a critical component of numerically controlled machine tools that directly affect machining precision and efficiency. The accurate identification of the modal parameters of an electric spindle is essential for optimizing design, enhancing dynamic performance, and facilitating fault diagnosis. This study proposes a covariance-driven stochastic subspace identification (SSI-cov) method integrated with a simulated annealing (SA) strategy and fuzzy C-means (FCM) clustering algorithm to achieve the automated identification of modal parameters for electric spindles. Using both finite element simulations and experimental tests conducted at 22 °C, the first five natural frequencies of the electric spindle under free, constrained, and dynamic conditions were extracted. The experimental results demonstrated experiment errors of 0.17% to 0.33%, 1.05% to 3.27%, and 1.29% to 3.31% for the free, constrained, and dynamic states, respectively. Compared to the traditional SSI-cov method, the proposed SA-FCM method improved accuracy by 12.05% to 27.32% in the free state, 17.45% to 47.83% in the constrained state, and 25.45% to 49.12% in the dynamic state. The frequency identification errors were reduced to a range of 2.25 Hz to 20.81 Hz, significantly decreasing errors in higher-order modes and demonstrating the robustness of the algorithm. The proposed method required no manual intervention, and it could be utilized to accurately analyze the modal parameters of electric spindles under free, constrained, and dynamic conditions, providing a precise and reliable solution for the modal analysis of electric spindles in various dynamic states.

Fractional Derivative to Symmetrically Extend the Memory of Fuzzy C-Means

The fuzzy C-means (FCM) clustering algorithm is a widely used unsupervised learning method known for its ability to identify natural groupings within datasets. While effective in many cases, FCM faces challenges such as sensitivity to initial cluster assignments, slow convergence, and difficulty in handling non-linear and overlapping clusters. Aimed at these limitations, this paper introduces a novel fractional fuzzy C-means (Frac-FCM) algorithm, which incorporates fractional derivatives into the FCM framework. By capturing non-local dependencies and long memory effects, fractional derivatives offer a more flexible and precise representation of data relationships, making the method more suitable for complex datasets. Additionally, a genetic algorithm (GA) is employed to optimize a new least-squares objective function that emphasizes the geometric properties of clusters, particularly focusing on the Fukuyama–Sugeno and Xie–Beni indices, thereby enhancing the balance between cluster compactness and separation. Furthermore, the Frac-FCM algorithm is evaluated on several benchmark datasets, including Iris, Seed, and Statlog, and compared against traditional methods like K-means, SOM, GMM, and FCM. The results indicate that Frac-FCM consistently outperforms these methods in terms of the Silhouette and Dunn indices. For instance, Frac-FCM achieves higher Silhouette scores of most cases, indicating more distinct and well-separated clusters. Dunn’s index further shows that Frac-FCM generates clusters that are better separated, surpassing the performance of traditional methods. These findings highlight the robustness and superior clustering performance of Frac-FCM. The Friedman test was employed to enhance and validate the effectiveness of Frac-FCM.

Grading of Traffic Interruptions in Highways to Tibet Based on the Entropy Weight-TOPSIS Method and Fuzzy C-Means Clustering Algorithm

The interruption of transportation on the way to Tibet has brought great losses to the Tibetan region. The work proposed a model that integrated the entropy weight-TOPSIS method with the fuzzy C-means clustering algorithm to discuss the causes and characteristics of traffic interruptions on the four main highways to Tibet. This approach aimed to quantify and grade traffic interruption states. First, the entropy weight-TOPSIS method was used to mitigate dimensions among various indices and quantitatively evaluate the status values of traffic interruptions. Then, the fuzzy C-means clustering algorithm was employed to grade these values. The proposed model graded traffic interruption states into four levels by evaluating the duration, mileage, and severity of traffic interruptions. Moreover, the four-level classification scheme can reflect the severity of traffic blocking events more precisely while maintaining a lower PE (Partition Entropy) value. In the four-level classification, the Sichuan–Tibet Highway and Xinjiang–Tibet Highway experienced more level-3 and level-4 serious interruptions, while most high-level interruptions on the Qinghai–Tibet Highway were classified as level-2 ordinary interruptions. The Yunnan–Tibet Highway, with limited data and primarily level-1 classification, was not analyzed in detail. These findings provide a reference for highway management departments to formulate targeted maintenance and emergency measures, especially the Sichuan–Tibet highway, which needs more attention and resource investment to improve its disaster resistance and reduce the impact of traffic interruptions.

A Comparison of K-Means and Fuzzy C-Means Clustering Algorithms for Clustering the Spread of Tuberculosis (TB) in the Lungs

A Comparison of K-Means and Fuzzy C-Means Clustering Algorithms for Clustering the Spread of Tuberculosis (TB) in the Lungs

Suppressed possibilistic fuzzy c-means clustering based on shadow sets for noisy data with imbalanced sizes

Possibilistic fuzzy c-means clustering (PFCM) algorithm generates fewer overlapping clustering centers than the possibilistic c-means clustering (PCM) algorithm and possesses better noise immunity than fuzzy c-means clustering (FCM) algorithm. However, with the increasing noise intensity and number of clusters, PFCM still faces the problem of getting partially overlapping clustering centers or mislocated centers in noise regions. Moreover, the sample-size imbalance increasingly intensifies the difficulty of positioning centers of small clusters. To solve the above problems, a suppressed possibilistic fuzzy c-means clustering algorithm based on shadow sets (S-SPFCM) is proposed by introducing the shadow set theory and the "suppressed competitive learning" strategy. Firstly, KL divergence is introduced in the objective function of PFCM to increase the anti-noise robustness of fuzzy memberships against long-range noise and outliers. Secondly, to reduce the number of overlapping centers caused by possibilistic memberships, the shadow set theory is introduced to divide each class adaptively into three regions (core, shadow, and exclusion regions) by an uncertainty balance method. The suppressed competitive learning method is extended by modifying the memberships of points within the three regions, thus artificially guiding the iterative track of clustering centers. Meanwhile, to further reduce the influence of imbalanced sizes, a scheme to reset mislocated centers in the core and shadow regions is also designed. In addition, to improve the segmentation performance of S-SPFCM for noisy images, a suppressed possibilistic fuzzy c-means clustering algorithm based on shadow sets and local information (SL-SPFCM) is also proposed. The SL-SPFCM first improves the Euclidean distance by a distance filtering scheme. Then SL-SPFCM introduces the local median membership of each pixel into the KL divergence of the objective function. Finally, experiments on synthetic datasets and color images which are characteristic of imbalanced sizes and noise injection demonstrate the proposed S-SPFCM and SL-SPFCM algorithms achieve smaller center deviations and higher clustering accuracy compared with several state-of-the-art clustering algorithms.

Clustering and Interpretation of time-series trajectories of chronic pain using evidential c-means

The most well-known unsupervised classification algorithms allow for the identification of hard or probabilistic partitions. However, when working with complex datasets such as those found in the healthcare domain, it is important to note that these partitions may have limitations, particularly when considering the uncertainty associated with medical data. Indeed, these algorithms do not account for modeling outliers, imprecise, or uncertain observations. In this study, we focused on analyzing time series of barometers attributes related to pain intensity in individuals with chronic pain. Our goal was to identify different typologies of care trajectories, highlighting distinct trajectories of chronic pain through clustering techniques. We propose a soft clustering approach based on feature extraction and selection from sequential data, aiming to improve interpretability and enhance the performance of the clustering procedure. Time series feature extraction and selection effectively handles complex and noisy data, improving interpretability and clustering performance. Evidential soft clustering models data uncertainty and imprecision, providing a better representation of nuances and variations in complex, raw data. The first step involves extracting features from the available time series and selecting the most important attributes. For this purpose, we use a time series feature extractor (Tsfresh) and unsupervised feature selection methods, including unsupervised Random Forest, Laplacian Score, and unsupervised Spectral Feature Selection. The second step involves using the evidential c-means (ECM) clustering algorithm on the extracted attributes. The ECM method, based on belief functions, allows for generating a credal partition that has the ability to model various forms of uncertainty. This partition can then be transformed into a hard partition to study individuals based on this uncertainty criterion. The results reveal the existence of two clusters of chronic pain related to discomfort and well-being, exhibiting excellent separability and compactness. Additionally, an uncertain cluster emerges grouping patients with intermediate characteristics. The interpretability of the determined partitions through descriptive analysis, statistical tests, and repeated measures multinomial regression on clinical and demographic data allowed us to determine the profile of patients in the identified trajectories.

Nighttime large-field video image change detection based on adaptive superpixel reconstruction and multi-scale singular value decomposition fusion

With the development of technology and the needs of social governance, surveillance equipment has been widely used. It is very mature to detect the change of surveillance video images in conventional scenes through video image change detection algorithms. However, in the large field of view environment at night, there are complex random noise and low signal-to-noise ratio in surveillance video images, which makes it difficult for people to find small moving targets. To this end, we propose a new method for nighttime large-field surveillance video image change detection based on adaptive superpixel reconstruction and multi-scale singular value decomposition fusion. The proposed method consists of two parts. On the one hand, an adaptive superpixel reconstruction method is used to reconstruct the two denoised difference images by selecting different segmentation parameters, and the edge information of the two reconstructed difference images is significantly enhanced. On the other hand, a multi-scale singular value decomposition fusion method is used to fuse the two difference images. The multi-scale singular value decomposition fusion obtains a robust difference image by selecting fusion rules at different scales and using the complementary information of different difference images, and the Fuzzy c-means (FCM) clustering algorithm is used to obtain the final changed image. Experimental results on a self-built nighttime large-field video image dataset with two resolutions show that the proposed method is superior to other algorithms in terms of detection accuracy and robustness.

Identification of Similar Weather Scenes in Terminal Areas Based on Multiresolution Spatiotemporal Windows

To accurately analyze the influence of similar weather scenes in the terminal area, a framework is proposed for identifying such scenarios based on the Multiresolution Spatiotemporal Window (MRSTW). The goal is to analyze the impact of similar weather patterns. This paper introduces a simple and effective method called the rasterized weather severity index (WSI) to reduce the dimensionality of data used for extracting air transport weather features, which can cause the loss of spatial information in an image. Additionally, the paper uses Dynamic Time Warping (DTW) and the Fuzzy C-mean (FCM) clustering algorithm to cluster time-series scenes influenced by convective weather in an unsupervised manner on a daily basis. The most similar weather scenes are then identified by searching for the same cluster within a multiresolution spatiotemporal window, using 4 h weather scenes as typical examples. Finally, the framework analyzes the impact of weather scenes on the operation of terminal area approach traffic flow by combining trajectory data. The findings demonstrate that this framework can effectively identify similar weather scenes and provide a more accurate reflection of their impact on the operation of terminal area approach traffic flow.

Hybrid Fuzzy C-Means Clustering Algorithm, Improving Solution Quality and Reducing Computational Complexity

Hybrid Fuzzy C-Means Clustering Algorithm, Improving Solution Quality and Reducing Computational Complexity

Distribution network line loss analysis method based on improved clustering algorithm and isolated forest algorithm

The long-term loss of distribution network in the process of distribution network development is caused by the backward management mode of distribution network. The traditional analysis and calculation methods of distribution network loss can not adapt to the current development environment of distribution network. To improve the accuracy of filling missing values in power load data, particle swarm optimization algorithm is proposed to optimize the clustering center of the clustering algorithm. Furthermore, the original isolated forest anomaly recognition algorithm can be used to detect outliers in the load data, and the coefficient of variation of the load data is used to improve the recognition accuracy of the algorithm. Finally, this paper introduces a breadth-first-based method for calculating line loss in the context of big data. An example is provided using the distribution network system of Yuxi City in Yunnan Province, and a simulation experiment is carried out. And the findings revealed that the error of the enhanced fuzzy C-mean clustering algorithm was on average − 6.35, with a standard deviation of 4.015 in the situation of partially missing data. The area under the characteristic curve of the improved isolated forest algorithm subjects in the case of the abnormal sample fuzzy situation was 0.8586, with the smallest decrease, based on the coefficient of variation, and through the refinement of the analysis, it was discovered that the feeder line loss rate is 7.62%. It is confirmed that the suggested technique can carry out distribution network line loss analysis fast and accurately and can serve as a guide for managing distribution network line loss.

Obstructive Sleep Apnea Detection from EEG Data: A Hybrid Approach of One-Dimensional Convolutional Neural Network and Enhanced Fuzzy C-Means Clustering Algorithm

Obstructive Sleep Apnea Detection from EEG Data: A Hybrid Approach of One-Dimensional Convolutional Neural Network and Enhanced Fuzzy C-Means Clustering Algorithm

Mapping winter wheat crop traits dynamic change and growth performance for variable rate application using Sentinel-1 and Sentinel-2

Site specific crop management for variable rate application is extensively recognized as a method for distributing agricultural input unevenly across a field, tailored to the diverse requirement of different areas. From the previous study, this approach proven to reduce agricultural input expenses by 10 % without impacting yield and ensure environmental sustainability. This study presents a new approach to delineate management zones for precision agriculture using crop biophysical property variability assessment within winter wheat fields. A multivariate random forest framework was developed to estimate winter wheat’s biophysical properties within fields from surface reflectance and backscatters of Sentinel-1 and Sentinel-2. Combining Sentinel-1 and Sentinel-2 data resulted in more precise estimation of the green area index (R²=0.98), aboveground dry biomass (R²=0.90), plant height (R²=0.94), and leaf nitrogen content (R²=0.78). Sentinel-2 alone was particularly effective in estimating shoot density (R²=0.94). These estimates were then used to create management zones for precision agriculture, classified based on agronomic performance benchmarks. The fuzzy c-mean clustering algorithm helped generate homogeneous management zones, considering the biophysical variations within fields.The ultimate goal is to integrate these biophysical property maps and management zones into crop management workflows. This integration will assist farmers in recognizing field variability and understanding its causes. Moreover, the spatial distribution of these zones supports variable rate application, guiding farmers towards more efficient, profitable, and sustainable crop management practices.

Adaptive feature selection and optimized multiple histogram construction for reversible data hiding

Reversible data hiding (RDH) algorithms have been extensively employed in the field of copyright protection and information dissemination. Among various RDH algorithms, the multiple histogram modification (MHM) algorithm has attracted significant attention because of its capability to generate high-quality marked images. In previous MHM methods, the prediction error histograms were mostly generated in a fixed way. Recently, clustering algorithms automatically classify prediction errors into multiple classes, which enhances the similarity among prediction errors within the same class. However, the design of features and the determination of clustering numbers are crucial in clustering algorithms. Traditional algorithms utilize the same features and fix the number of clusters (e.g., empirically generate 16 classes), which may limit the performance due to the lack of adaptivity. To address these limitations, an adaptive initial feature selection scheme and a clustering number optimization scheme based on the Fuzzy C-Means (FCM) clustering algorithm are proposed in this paper. The superiority of the proposed scheme over other state-of-the-art schemes is verified by experimental results.

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.

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

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

Soil Organic Carbon Prediction Based on Vis-NIR Spectral Classification Data Using GWPCA-FCM Algorithm.

Soil visible and near-infrared reflectance spectroscopy is an effective tool for the rapid estimation of soil organic carbon (SOC). The development of spectroscopic technology has increased the application of spectral libraries for SOC research. However, the direct application of spectral libraries for SOC prediction remains challenging due to the high variability in soil types and soil-forming factors. This study aims to address this challenge by improving SOC prediction accuracy through spectral classification. We utilized the European Land Use and Cover Area frame Survey (LUCAS) large-scale spectral library and employed a geographically weighted principal component analysis (GWPCA) combined with a fuzzy c-means (FCM) clustering algorithm to classify the spectra. Subsequently, we used partial least squares regression (PLSR) and the Cubist model for SOC prediction. Additionally, we classified the soil data by land cover types and compared the classification prediction results with those obtained from spectral classification. The results showed that (1) the GWPCA-FCM-Cubist model yielded the best predictions, with an average accuracy of R2 = 0.83 and RPIQ = 2.95, representing improvements of 10.33% and 18.00% in R2 and RPIQ, respectively, compared to unclassified full sample modeling. (2) The accuracy of spectral classification modeling based on GWPCA-FCM was significantly superior to that of land cover type classification modeling. Specifically, there was a 7.64% and 14.22% improvement in R2 and RPIQ, respectively, under PLSR, and a 13.36% and 29.10% improvement in R2 and RPIQ, respectively, under Cubist. (3) Overall, the prediction accuracy of Cubist models was better than that of PLSR models. These findings indicate that the application of GWPCA and FCM clustering in conjunction with the Cubist modeling technique can significantly enhance the prediction accuracy of SOC from large-scale spectral libraries.

Design and Development of an Efficient Demographic-based Movie Recommender System using Hybrid Machine Learning Techniques

Movie Recommender systems are frequently used in academics and industry to give users with relevant, engaging material based on their rating history. However, most traditional methods suffer from the cold-start problem, which is the initial lack of item ratings and data sparsity. The Hybrid Machine Learning (ML) technique is proposed for a movie recommendation system. Demographic data is collected from the Movie Lens dataset, and attributes are evaluated using the Attribute Analysis module. The Aquila Optimization Algorithm is used to select the best attributes, while Random Forest classifier is used for classification. Data is clustered using Fuzzy Probabilistic Cmeans Clustering Algorithm (FPCCA), and the Correspondence Index Assessment Phase (CIAP) uses Bhattacharyya Coefficient in Collaborative Filtering (BCCF) for similarity index calculation. The Outcomes gives the proposed method obtained low error, such as MAE has 0.44, RMSE has 0.63 compared with the baseline methods.

Visualization analysis of educational data statistics based on big data mining

The visual analysis method of educational data statistics based on big data mining is studied to improve students’ academic performance. Introducing the Mahalanobis distance and covariance matrix into the Fuzzy C-Means (FCM) clustering algorithm improves the FCM clustering algorithm. Through the improvement of the FCM clustering algorithm, the education data is mined from the massive original education data. The mining results are analyzed statistically, and the statistical analysis chart of education data is drawn. By improving the force-guided layout algorithm, the mined educational data points are written into the elastic graph layout to realize the visual layout. The ECharts data visualization analysis component presents the visual layout results of education data points and the statistical analysis charts of education data. Experiments show that this method can effectively mine educational data and draw statistical analysis charts of educational data. Among them, learning analysis data occupy the highest proportion (15%), and privacy protection data occupy the lowest proportion (only 1%). The method can effectively lay out the educational data points and has a better visual effect. This method can effectively present the results of statistical analysis of educational data in visual form, in which learning analysis data is the most important.

Robust Fuzzy C-Means Clustering Algorithm Based on Normal Shrink and Membership Filtering for Image Segmentation

The robustness and effectiveness of image segmentation using the FCM algorithm can be improved by incorporating local spatial information into the FCM method, which is particularly noise-tolerant. However the introduction of local spatial information gives more computational complexity. Hence to overcome this problem an improved FCM clustering method is proposed which is based on a normal shrink algorithm with membership filtering. The Proposed method gives a faster and more robust result in comparison to FCM. Firstly, a normal shrink denoising algorithm is introduced to preserve the image details and noise immunity. Secondly, membership filtering is introduced, which depends only on the local spatial neighboring properties of the matrix called the membership partition matrix. The Proposed method is faster and simpler as it does not calculate the distance between pixels and cluster centers and between local spatial neighboring. Also, it is very efficient for noise immunity.