C-means Clustering Algorithm Research Articles

Interval type-2 fuzzy c-means collaborative clustering approach for scientific cloud workflows

Scientific workflows and other big data applications requiring large amounts of data may be shared through cloud computing. Big data processing with scientific workflows is costly regarding bandwidth, execution time, and data transmission. To reduce these expenses, a type-2 fuzzy set-based data collaboration technique is proposed in this research. It lowers the cost of processing massive amounts of data and optimizes data placement. The suggested method examines the datasets inside each data center using data dependencies, groups the datasets using the Interval Type-2 Fuzzy C-Means (IT2FCM) clustering algorithm and then reorganizes the clusters according to data collaboration. With superior outcomes compared to prior techniques, our proposed method of using type-2 fuzzy sets to accomplish collaborative clustering may assist in dealing with data uncertainties and lower the total quantity of data placement.

Transformers for aerial images semantic segmentation of natural disaster-impacted areas in natural disaster assessment

Aerial image segmentation of natural disaster-impacted areas and detailed and automatic natural disaster assessment are the main focus of this study. Detecting and recognizing objects on aerial images of areas impacted by natural disasters and assessing natural disaster-impacted areas are still difficult problems. To solve these problems, this study utilizes four of the latest transformer-based semantic segmentation network models, bidirectional encoder representation from image transformers (BEIT), dense prediction transformer (DPT), OneFormer, and SegFormer, and proposes a detailed and automatic natural disaster assessment of the segmented image. The SegFormer model achieved the first-best result, and the OneFormer model achieved the second-best result. The SegFormer model outperformed OneFormer by 1.58% higher for the mean accuracy value and 4.28% for the mean intersection over union (mIoU) value. All receiver operating characteristics (ROC) curves have mean area under curve (AUC) values above 0.9, which means that the SegFormer model performs well in generating semantic segmentation images. The fuzzy c-means (FCM) clustering algorithm performed well and could automatically cluster the natural disaster assessments into four categories. This study has produced semantic segmentation of aerial images of areas impacted by natural disasters and natural disaster assessments, which can be used in natural disaster management systems.

A novel competitive cuckoo search algorithm for node placement in wireless sensor networks

Wireless sensor networks (WSNs) are essential for many different types of applications, including industrial, commercial, and agricultural. Inadequate node location, dynamic nodes, network longevity, increased packet drop rates, scaling problems, limited adaptability, and changing climatic conditions pose challenges to the WSN's efficacy. Numerous bio-inspired algorithms have been previously introduced for node placement, demonstrating a significant improvement in data aggregation performance. However, because of the low variability in the solution, weak convergence, and poor balance between exploitation and exploration, the results of WSNs are challenging to interpret. With a unique competitive Cuckoo search algorithm (CCSA), this research presents a connectivity-aware, energy- efficient, and node placement method. Using the elite population to increase the variety of the answer, the suggested competitive strategy aims to enhance convergence. It additionally employs the fuzzy C-mean clustering algorithm. The clustering optimization based on cluster head energy, density, location, Gini coefficients, and other comparable factors uses an artificial bee colony optimization (ABC) algorithm to enhance the clustering and cluster head position. A comparison between the recommended scheme and the traditional state-of-the-art reveals that the suggested system performs better regarding network throughput, residual energy, and network lifetime.

Early warning strategies for corporate operational risk: A study by an improved random forest algorithm using FCM clustering.

To enhance the accuracy and response speed of the risk early warning system, this study develops a novel early warning system that combines the Fuzzy C-Means (FCM) clustering algorithm and the Random Forest (RF) model. Firstly, based on operational risk theory, market risk, research and development risk, financial risk, and human resource risk are selected as the primary indicators for enterprise risk assessment. Secondly, the Criteria Importance Through Intercriteria Correlation (CRITIC) weight method is employed to determine the importance of these risk indicators, thereby enhancing the model's prediction ability and stability. Following this, the FCM clustering algorithm is utilized for pre-processing sample data to improve the efficiency and accuracy of data classification. Finally, an improved RF model is constructed by optimizing the parameters of the RF algorithm. The data selected is mainly from RESSET/DB, covering the issuance, trading, and rating data of fixed-income products such as bonds, government bonds, and corporate bonds, and provides basic information, net value, position, and performance data of funds. The experimental results show that the model achieves an F1 score of 87.26%, an accuracy of 87.95%, an Area under the Curve (AUC) of 91.20%, a precision of 89.29%, and a recall of 87.48%. They are respectively 6.45%, 4.45%, 5.09%, 4.81%, and 3.83% higher than the traditional RF model. In this study, an improved RF model based on FCM clustering is successfully constructed, and the accuracy of risk early warning models and their ability to handle complex data are significantly improved.

Multi-UAV Delivery Path Optimization Based on Fuzzy C-Means Clustering Algorithm Based on Annealing Genetic Algorithm and Improved Hopfield Neural Network

This study develops an MTSP model for multi-UAV delivery optimization from a central hub, proposing a hybrid algorithm that integrates genetic simulated annealing-enhanced clustering with an improved Hopfield neural network to minimize the total flight distance. The proposed methodology initially employs an enhanced fuzzy C-means clustering technique integrated with genetic simulated annealing (GSA) to effectively partition the MTSP formulation into multiple discrete traveling salesman problem (TSP) instances. The subsequent phase implements an enhanced Hopfield neural network (HNN) architecture incorporating three key modifications: data normalization procedures, adaptive step-size control mechanisms, and simulated annealing integration, collectively improving the TSP solution quality and computational efficiency. The proposed algorithm’s effectiveness is validated through comprehensive case studies, demonstrating significant performance improvements in the computational efficiency and solution quality compared to conventional methods. The results show that during clustering, the improved clustering algorithm is more stable in its clustering effect. With regard to path optimization, the improved neural network algorithm has a higher computational efficiency and makes it easier to obtain the global optimal solution. Compared with the genetic algorithm and ant colony algorithm, its iteration times, path length, and delivery time are reduced to varying degrees. To sum up, the hybrid optimization algorithm has obvious advantages for solving a multi-UAV collaborative distribution path optimization problem.

Highway Typical Scenario Operation and Maintenance Energy Demand Forecasting

Highways play a critical role in global energy transitions and climate change mitigation, making the accurate forecasting of operational energy demand essential for improving energy efficiency and promoting green energy applications. This study develops a multi-scenario energy demand forecasting model focused on five key operational contexts: service areas, tunnels, toll stations, management centers, and roadside facilities. The model integrates user characteristics, behavioral patterns, and meteorological data, employing agent-based modeling (ABM) and the fuzzy C-means (FCM) clustering algorithm to simulate and analyze energy demand. Results indicate that during major holidays, total daily electricity consumption and peak demand increase by 143.2% and 43.8%, respectively, compared to baseline conditions. Conversely, during snowfall events, total electricity consumption and peak demand decrease by 8.8% and 11.7%, respectively. These findings provide valuable data support and a scientific basis for sustainable energy management in highway operations, contributing to the broader application of green energy solutions.

Behavioral Correlation-Based Residential Space Modularization Using Design Structure Matrix and Fuzzy C-Means Clustering Algorithm

This study introduces an automated method for constructing residential functional modules from the perspective of user behavior. By integrating the design structure matrix (DSM) and Fuzzy C-Means Clustering Algorithm (FCM), this approach systematically explores architectural functional modules. The DSM is employed to statistically analyze the correlations between residential behaviors. These correlations are then processed using FCM to generate various module segmentation schemes. The optimal scheme is selected based on modularity calculations. The results demonstrate improved modularity compared to traditional room-based designs, offering a greater variety of combinations and hierarchical organization. This methodology provides architects with a novel approach to address space integration challenges.

Long-term creep life prediction of P91 steel using domain knowledge and back propagation artificial neural network

ABSTRACT In this work, the long-term creep life of P91 steels with the limited experiment data was predicted using the domain knowledge and back propagation artificial neural networks (BP-ANN). Based on the traditional creep life models of Larson-Miller (L-M) parameter and θ projection, the domain knowledge was incorporated to expand the creep dataset. Then, the fuzzy C-means (FCM) clustering algorithm was introduced for training data collection. Consequently, the creep life prediction was conducted with the corresponding dataset and BP-ANN. The obtained results demonstrated that in contrast to the conventional prediction models of creep life, the prediction accuracy for long-term creep life of P91 steels can be improved significantly by the present model. For instance, the predicted creep lives 200,000 h exhibit the average errors of 6.0%. In addition, the present study offers a convenient tool to solve the issue of limited experiment data, particularly the long-term creep of P91 steels.

DDoS Attack Detection Using Machine Learning and Improved Clustering Algorithm

Distributed Denial of Service (DDoS) attacks have recently emerged as one of the most destructive threats to network systems. This paper aims to develop a technique that efficiently identifies DDoS attacks in networked systems by leveraging improved clustering techniques and machine learning algorithms. This methodology employs a Modified Fuzzy C-Means (MFCM) clustering algorithm to partition the available DDoS attack dataset and integrate a classification algorithm to accurately detect attacks and classify data based on specific network characteristics derived from the transformed data packets. The clustering algorithm predominantly relies on distance measurements derived from fuzzy coefficients, significantly limiting its ability to identify and classify emerging attack scenarios. The current study introduces the integration of the MFCM clustering algorithm with sophisticated classification techniques to enhance accuracy and minimize errors. The efficacy of the modified clustering algorithm was evaluated using the entropy criterion, and a value of 0.99 was attained, demonstrating superior performance relative to traditional algorithms. The training algorithm was rigorously evaluated utilizing established performance metrics, such as accuracy, detection rate, and false positive rate. The results indicate that the accuracy improved consistently across all classification algorithms applied, contributing to an enhanced attack detection rate.

Clustering Of Stunting Prevalence with Gender Equality in Households Index and Socio-Demographic Variables In Regency/Cities Of Bali, West Nusa Tenggara And East Nusa Tenggara Provinces

Abstract The issue of stunting is a primary focus of efforts to enhance the quality of human resources. The objective of the present study is to construct a gender equality index within households by grouping them according to the prevalence of stunting and other social indicators in the regencies/cities of Bali, West Nusa Tenggara, and East Nusa Tenggara provinces. The methodology used is based on the structural equation modelling partial least squares (SEM-PLS) approach and the Fuzzy C-Means (FCM) clustering algorithm, which were employed in the 2017 Indonesia Demographic and Health Survey (IDHS), the 2021 Indonesia Nutritional Status Survey, and the data published by the Statistics Indonesia (BPS). The province of Bali has the highest average Gender Equality Household Index score. The index size for low stunting prevalence clusters is higher than for high stunting prevalence clusters. The results indicated that regencies/cities with a high prevalence of stunting exhibited poor environmental sanitation conditions and lower Human Development Index (HDI) and Gender Equality Index (GEI) scores. Concurrently, there was a notable exclusive breastfeeding and low birth weight (LBW) prevalence. The formulation of this later can facilitate stakeholders in developing a roadmap to accelerate stunting control.

Point-of-interest recommender model using geo-tagged photos in accordance with imperialist Fuzzy C-means clustering.

Although recommender systems (RSs) strive to provide recommendations based on individuals' histories and preferences, most recommendations made by these systems do not utilize location and time-based information. This paper presents a travel recommender system by integrating the Imperialist Competitive Algorithm (ICA) and Fuzzy C-Means (FCM) Clustering algorithm. Compared to similar studies, this recommender system takes into account more POIs, including location, number of visits, weather conditions, time of day, user mood, traffic volume, season, and temperature. The effectiveness and accuracy of the proposed method are assessed using the Flickr dataset, indicating that it is able to provide effective and accurate recommendations that are compatible with the user's interests and the current status of his/her visit. Results showed that, precision and Mean Absolute Precision (MAP) in the proposed method have been grown 23.6% and 23.72% in comparison to Popularity Rank, 28.98% and 19.67% in comparison to Classic Rank and 18.66% and 19.67% in comparison to Frequent Rank methods. Also, Mean Absolute Error (MAE) index in proposed method has been improved 60.71%, 64.51% and 56% in comparisons to the Popularity Rank, Classic Rank and Frequent Rank methods respectively.

Exploration of Computer Vision Technology and Legal Consideration of Online False Propaganda Behavior in the Perspective of Civil and Commercial Law

Abstract False propaganda behavior seriously misleads consumers, and the general people are deeply hurt by this, so the monitoring of online propaganda behavior has become an urgent issue. This paper introduces a method for monitoring false propaganda behavior based on computer vision technology. A video capture card is used to capture network signals for subsequent image recognition. For the characteristics of video content recognition, the “first and last key extraction method” is used to extract key frames. Then the selected key frames are preprocessed for binarization. In the binarization module of the system, the fuzzy C-mean clustering algorithm is designed and applied for image binarization in this paper. Finally, the template-based matching method is used for image recognition, and it has been appropriately improved in this module to enhance its efficiency and accuracy in recognizing false propaganda behaviors on the Internet. On the basis of the above work, this topic in the civil and commercial law vision is from three aspects about the network false behavior propaganda considerations, in order to make this aspect of the law and regulations more perfect. In this paper, the method was used to carry out image testing experiments on false network propaganda behavior. The experimental results show that the recognition accuracy of the method for different types of false propaganda behavior is 90% and above, and the recognition accuracy of the false propaganda behavior of the user evaluation category is as high as 99%. This indicates that the method holds practical significance in addressing online false propaganda behavior.

Research on the Innovation Path of Civic Education for College Students under the New Media Environment

Abstract By virtue of its ability to adapt to the psychological characteristics of college students, new media has now become a key carrier for the development of Civic and Political Education in colleges and universities. This paper explains the algorithmic recommendation mechanism of new media from multiple perspectives, the opportunities and challenges for college students’ civic education, and proposes a personalized recommendation algorithm for college students’ civic education based on LDA model. On the basis of similarity calculation, a user behavior feature extraction model is established. And combined with the fuzzy C-mean clustering algorithm, the joint information entropy of new media user behavior is calculated, and the joint information entropy results are optimized for the new media recommendation algorithm. Finally, the effectiveness and problems of the new media environment in the ideological and political education of college students are empirically analyzed. The results show that for the new media communication of college students’ Civic and Political Education, 22.04 percent of the students gave a satisfactory answer of 5 points in the content dimension. It shows that in the new media environment, the content of the Civic and Political Education still needs to be constantly innovated, and actively explore the content that is compatible with the physical and mental development and characteristics of college students.

The construction of a multi-platform system for teaching journalism and communication based on big data technology

Abstract Building experimental teaching platforms and analyzing student learning data to help improve instructional design are trends in the context of the big data era. This paper combines the innovative multi-type practical teaching theory to build an all-media experimental platform with three departments, namely, gathering department, editorial department and production department, and makes it available to journalism learners. The fuzzy C-mean clustering algorithm based on DTW is designed and applied to analyze the data of the all-media experimental platform. Combine the platform data to analyze the types of learners and the characteristics of learning behaviors of each category, and provide teaching improvement methods for teachers based on the relevant analysis results. By analyzing the data of the all-media experimental platform, it can be seen that there is an overall correlation between the students’ learning performance and the number of days of using the platform, the number of interactions, as well as the number of chapters studied, the number of forum speeches, and the number of horizontal playbacks. Students were further categorized into a total of six categories such as focused learners, continuous learners, etc. In which continuous learners accounted for the largest proportion of 38%, while abandoned learners accounted for the smallest proportion of only 2%, and other categories of learners accounted for between 4% and 25%. Improving teaching planning based on the results of platform data analysis will effectively improve the skill level of news communication learners and lay the foundation for their subsequent path to the news communication profession.

Classification and Recognition of Music Curriculum Resources Based on Improved Fuzzy C-Means Clustering Algorithm

Classification and Recognition of Music Curriculum Resources Based on Improved Fuzzy C-Means Clustering Algorithm

Customer Segmentation Using the RFMD Model and Fuzzy C-Means Algorithm

Many businesses face challenges in optimizing customer data processing, which often limits the ability to understand customer behavior and improve marketing strategies. This research addresses these challenges by applying the RFMD (Recency, Frequency, Monetary, Diversity) model combined with the Fuzzy C-Means (FCM) clustering algorithm to segment customers based on transaction data. The results identified five distinct customer segments based on Customer portfolio Analysis (CPA), which were validated using the Davies-Bouldin Index (DBI), with each segment showing diverse levels of engagement and behavioral patterns. The results show that the best clusters of Superstar and Golden customers are clusters 4 and 2, while Typical and Occasional customers are clusters 1 and 3. The lowest cluster of Everyday customers is found in cluster 5. The findings provide applicable insights to improve customer retention and optimize data-driven marketing strategies.

Module Configuration of Rail Freight Transportation with Both Customer Segmentation and Product Family Genealogy in China

The Chinese government is actively restructuring transportation to shift towards more sustainable rail freight transportation (RFT); however, there is still a lack of more systematic optimization in the whole production chain. This study develops a dual-focus modular configuration approach to explore the integration of customer demand and the production chain to achieve more sustainable operations in RFT. Customers have yielded eleven distinct groups, and operational processes have been segmented into sixteen modules by using the Ant Colony Optimization-based Fuzzy C-Means Clustering (ACOFCM) algorithm. Consequently, a Product Family Genealogy (PFG) model is conducted to identify three tailored product families (i.e., cross-border, multi-modal and general freight product). The developed dual-focus modular configuration approach has been proven to be feasible by utilizing a backtracking algorithm through a case study in an RFT logistics enterprise in China, which provides a standardization and optimization for RFT modular configurations.

An Unsupervised Remote Sensing Image Change Detection Method Based on RVMamba and Posterior Probability Space Change Vector

Change vector analysis in posterior probability space (CVAPS) is an effective change detection (CD) framework that does not require sound radiometric correction and is robust against accumulated classification errors. Based on training samples within target images, CVAPS can generate a uniformly scaled change-magnitude map that is suitable for a global threshold. However, vigorous user intervention is required to achieve optimal performance. Therefore, to eliminate user intervention and retain the merit of CVAPS, an unsupervised CVAPS (UCVAPS) CD method, RFCC, which does not require rigorous user training, is proposed in this study. In the RFCC, we propose an unsupervised remote sensing image segmentation algorithm based on the Mamba model, i.e., RVMamba differentiable feature clustering, which introduces two loss functions as constraints to ensure that RVMamba achieves accurate segmentation results and to supply the CSBN module with high-quality training samples. In the CD module, the fuzzy C-means clustering (FCM) algorithm decomposes mixed pixels into multiple signal classes, thereby alleviating cumulative clustering errors. Then, a context-sensitive Bayesian network (CSBN) model is introduced to incorporate spatial information at the pixel level to estimate the corresponding posterior probability vector. Thus, it is suitable for high-resolution remote sensing (HRRS) imagery. Finally, the UCVAPS framework can generate a uniformly scaled change-magnitude map that is suitable for the global threshold and can produce accurate CD results. The experimental results on seven change detection datasets confirmed that the proposed method outperforms five state-of-the-art competitive CD methods.

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.