Fuzzy C-means Algorithm Research Articles

Fuzzy Clustering Approach to Consumer Behavior Analysis Based on Purchasing Patterns

Consumer behaviour analysis is critically important to contemporary marketing strategy, allowing for an understanding of purchasing patterns and design of targeted interventions. Traditional segmentation methods like K-means and hierarchical clustering are often not sufficient as they force consumers into a single cluster, though life is not set in stone and real worldbehaviour is continuously overlapping. This research utilizes fuzzy clustering (machine learning) to segment consumers across Product Category Preference (PCP), Price Sensitivity (PS) and Purchase Frequency (PF) using the Fuzzy C-Means (FCM) algorithm. The report identifies two underlying consumer segments: High Spend Customers, who have strong product preference, purchase often, and are not sensitive to price, and Bargain Shoppers who are price-sensitive and make infrequent purchases with low product preference. Fuzzy clustering assigns each customer as a member to each of the segments with a value between 0 and 1, rather than in a traditional, discrete method of forcing segmentation, capturing the flexible, context-dependent nature of purchasing behaviour. In this process, the model iterates over the computation of cluster centroids and updates cluster membership until it reaches a stable state. The study finds that fuzzy clustering is more representative of the hybrid and uncertain nature of consumer behaviour which provides businesses with the benefit of adapting marketing strategies accordingly. Beyond the segmentation of consumers, fuzzy clustering may also find application in personalization, recommendation engines (including recommendation and personalization of products), dynamic pricing (where customers are targeted on special offers), loyalty programs, and optimization of the retail space (to better serve customers using both traditional and modern selling formats).

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.

Resilient by nature or technology? How Industry 4.0 enhances Supply Chain Resilience until 2035

Purpose Global events revealed the vulnerability of global supply chain (SCs) and triggered the debate about how to improve supply chain resilience (SCRES). Industry 4.0 (I4.0) provides promising opportunities. However, there is still great uncertainty about its future implementation. Hence, this study aims to identify the potential of integrating I4.0 technologies to improve SCRES. Design/methodology/approach Based on current literature and grounded in the organizational information processing theory as a theoretical lens, 12 future-oriented projections on the implication of I4.0 on supply chain risk management (SCRM) were developed. A two-round Delphi study among 49 SC management experts from industry, academia and professional service companies was conducted to assess and discuss the projections regarding their expected probability of occurrence in 2035, their impact on SCRES and their desirability. A fuzzy c-means algorithm was applied to cluster the projections based on expert assessments. Findings Based on the experts’ assessments, three clusters of I4.0 influence on SCRES were identified. First, the study suggests that in 2035, companies will have integrated an SCRM perspective into their strategic decision-making and their daily operations. Second, companies strive for collaborative SCRM, based on I4.0-enhanced risk information sharing, but struggle with full implementation until 2035. Third, I4.0 technologies will support, but not replace, SC risk managers in making risk-related decisions in 2035. Originality/value Thus, this study addresses the necessity for future-oriented empirical research on I4.0-enhanced SCRM and analyzes how SCRES can be improved through the combination of cultural, personnel and strategic factors supported by I4.0 technologies in the long term.

Application and exploration in intelligent recruitment in personnel management system of universities based on big data models

Traditional personnel recruitment methods are often inefficient and struggle to find candidates who meet job requirements. In this paper, we first develop a comprehensive personnel management system for colleges and universities that streamlines the recruitment process and information management. Next, recruitment data from the system is analyzed using the fuzzy C-means algorithm to cluster applicant profiles and extract position-specific user characteristics. Finally, a joint embedded neural network is employed to match applicant profiles with job positions by optimizing an objective function. Experimental results demonstrate a high job matching rate (up to 98.1%), a significantly reduced recruitment cycle (from job posting to candidate onboarding in 25 days), and a system response time as low as 0.5 seconds. These findings highlight the effectiveness of big data technology in providing timely feedback, reducing recruitment costs and staff workload, and promoting the intelligent development of talent recruitment.

Enhanced novelty approaches for resource allocation model for multi-cloud environment in vehicular Ad-Hoc networks

As the number of service requests for applications continues increasing due to various conditions, the limitations on the number of resources provide a barrier in providing the applications with the appropriate Quality of Service (QoS) assurances. As a result, an efficient scheduling mechanism is required to determine the order of handling application requests, as well as the appropriate use of a broadcast media and data transfer. In this paper an innovative approach, incorporating the Crossover and Mutation (CM)-centered Marine Predator Algorithm (MPA) is introduced for an effective resource allocation. This strategic resource allocation optimally schedules resources within the Vehicular Edge computing (VEC) network, ensuring the most efficient utilization. The proposed method begins by the meticulous feature extraction from the Vehicular network model, with attributes such as mobility patterns, transmission medium, bandwidth, storage capacity, and packet delivery ratio. For further analysis the Elephant Herding Lion Optimizer (EHLO) algorithm is employed to pinpoint the most critical attributes. Subsequently the Modified Fuzzy C-Means (MFCM) algorithm is used for efficient vehicle clustering centred on selected attributes. These clustered vehicle characteristics are then transferred and stored within the cloud server infrastructure. The performance of the proposed methodology is evaluated using MATLAB software using simulation method. This study offers a comprehensive solution to the resource allocation challenge in Vehicular Cloud Networks, addresses the burgeoning demands of modern applications while ensuring QoS assurances and signifies a significant advancement in the field of VEC.

An integrated study of hard and soft cluster analyses for detecting leachate in a MSW landfill site using geoelectrical data.

An integrated study of hard and soft cluster analyses for detecting leachate in a MSW landfill site using geoelectrical data.

A synergistic approach to material analysis and power source engineering in ultrasonic welding of polymers

Ultrasonic welding is an energy-efficient and effective technique for joining thermoplastic polymers and composites. It utilizes ultrasonic frequency vibrations to generate frictional and viscous heating, facilitating molecular diffusion bonding between surfaces. Key controlled parameters in this process include supply frequency and amplitude, weld time, hold time and static clamp pressure. This study presents experimental trials, governing process equations, simulation studies, and image processing results for ultrasonic welding of 150 × 50 mm polymer materials with a thickness of 5 mm. The investigation explored the impact of energy director configurations and process parameters on weld quality. Findings highlighted the dependency of ultrasonic weld quality on these controlled parameters, underscoring the importance of optimizing static clamp pressure and offering new insights into energy director usage. Additionally, the study used image processing for weld defect identification and compared image processing algorithms—Fuzzy C-Means (FCM) and Otsu. FCM algorithm was found effective in the comparative study. Simulation of the piezoelectric converter system illustrated how vibrational amplitude is influenced by power supply and frequency. The conceptual exploration of temperature gradients, along with simulation analyses and experimental observations, lays the groundwork for advanced research in ultrasonic welding. This study provides interdisciplinary insights into polymer thermal gradient behavior and outlines further research needs for designing an efficient ultrasonic polymer welding machine.

Chan-Vese aided fuzzy C-means approach for whole breast and fibroglandular tissue segmentation: Preliminary application to real-world breast MRI.

Magnetic resonance imaging (MRI) is a highly sensitive modality for diagnosing breast cancer, providing an expanding range of clinical usages that are crucial for the care of women at elevated risk of breast cancer development. Segmentation of the whole breast and fibroglandular tissue (FGT), used to evaluate breast cancer risk, is often manually delineated by radiologists in clinical practice. In this paper, we aim to substitute handcrafted breast density segmentation and categorization. The traditional fuzzy C-means (FCM) enable automatic segmentation but may be susceptible to heterogeneity or sparse FGT distribution in MRI. We develop a new automated technique for the segmentation of whole breast and FGT for the coronal-view MRI. We propose a Chan-Vese (CV) aided FCM segmentation approach for estimating the FGT in the whole breast using fat-suppressed (FS) precontrast T1-weighted breast MRI. We present a methodology pipeline comprising region-of-interest (ROI) extraction, nonparametric non-uniform intensity normalization N4 algorithm-based intensity inhomogeneity correction, skin-layer extraction, and then whole breast and FGT segmentation. Our approach involves the FCM algorithm to assign membership degrees to pixels, distinguishing FGT regions from surrounding adipose tissues by assessing their probability of belonging to specific FGT regions, and subsequently, the region-based active contour CV model leverages these membership degrees to direct contour evolution and enhance segmentation boundaries. The proposed method adeptly tackles common challenges in MRI, including blurred edges, low contrast, and intensity inhomogeneity, with efficiency. We evaluated our approach on the Duke Breast Cancer MRI data (DBCM-data) and achieved good segmentation accuracy in terms of Dice similarity coefficient (DSC), Intersection-over-Union (IoU), and Sensitivity (SEN). Our method demonstrates significant accuracy, achieving a DSC (%) of 93.2 ± 3.3 and 84.1 ± 4.9, IoU (%) of 86.4 ± 3.5 and 73.2 ± 5.1, and SEN 87.3 ± 4.1 and 76.7 ± 4.1 for the segmentations of whole breast and FGT, respectively. Our results demonstrated that the CV-aided FCM approach significantly outperformed the existing methods and resulted in significantly more accurate whole breast and FGT segmentation in MRI data.

Determination of biomass energy potential based on regional characteristics using adaptive clustering method

Determining the energy potential of biomass is the first step in selecting the most suitable and efficient energy conversion technology based on regional characteristics. The approach to estimating and determining biomass potential generally uses geospatial technology related to collecting and processing data about mapping an area. Unfortunately, this method is inadequate for simulating the interaction between variables, nor can it provide accurate predictions for the biomass supply chain. As a result, the results obtained from this method tend to be biased and macro, particularly in regions experiencing rapid land-use development. In this paper, the author has developed a clustering methodology with a fuzzy c-means (FCM) algorithm to determine biomass energy potential based on regional characteristics to produce data clusters with high accuracy. Grouping the characteristics of clustering-based areas involves grouping physical or abstract objects into classes or similar objects.

Density based spatial clustering of applications with noise and fuzzy C-means algorithms for unsupervised mineral prospectivity mapping

Density based spatial clustering of applications with noise and fuzzy C-means algorithms for unsupervised mineral prospectivity mapping

Classification of human monkeypox with the Fuzzy C-Means Algorithm using image processing methods and Haralick texture parameters

Aim: Human monkeypox can cause skin lesions in the form of blisters of different shapes on various parts of the body. Due to the fact that the skin lesions caused by human monkeypox have a very similar appearance to lesions caused by chickenpox and measles, the study includes images of chickenpox and measles as well as images of human monkeypox. The aim of this study is to distinguish human monkeypox virus skin lesion images from other viral diseases with similar images. Methods: For this study, the Monkeypox Skin Lesion Dataset, which consists of binary classification data for monkeypox and non-monkeypox (chickenpox, measles) skin lesions, is accessed from the Kaggle.com website. In total, 228 images are processed, with 101 images in the monkeypox group and 127 images in the non-monkeypox group. The images in the Monkeypox Skin Lesion Dataset are processed using image analysis methods and Haralick texture parameters are calculated to create 13 different features for each image. For the classification process in the statistical analysis part of the study, Fuzzy C-Means algorithm is used. Results: The images used in the study belong to individuals with varying skin tones and from different parts of the body, and the algorithm provides encouraging results in determining the type of skin lesions in the images. The overall classification accuracy rate is 61.8%, and the highest accuracy (76.2%) is achieved in the monkeypox class. Conclusion: This study demonstrates that images of viral diseases with similar skin lesions can be classified using various image-processing techniques and different statistical methods.

Fuzzy C-Means Algorithm for Grouping Students Based on Preferences and Academic Potential

Fuzzy C-Means Algorithm for Grouping Students Based on Preferences and Academic Potential

Unconstrained Fuzzy C-Means Algorithm.

Fuzzy C-Means algorithm (FCM) is one of the most commonly used fuzzy clustering algorithm, which uses the alternating optimization algorithm to update the membership matrix and the cluster center matrix. FCM achieves effective results in clustering tasks. However, due to many constraints, the objective function is inconvenient to optimize directly and is prone to converges to a suboptimal local minimum, which affects the clustering performance. In this paper, we propose a minimization problem equivalent to FCM. Firstly, we use the optimal solution when fixing the cluster center matrix to replace the membership matrix, transforming the original constrained optimization problem into an unconstrained optimization problem, thus reducing the number of variables. We then use gradient descent instead of alternating optimization to solve the model, so we call this model UC-FCM. Extensive experimental results show that UC-FCM can obtain better local minimum and achieve superior clustering performance compared to FCM under the same initialization. Moreover, UC-FCM is also competitive compared with other advanced clustering algorithms.

An efficient method for privacy protection in big data analytics using oppositional fruit fly algorithm

This work employs anonymization techniques to safeguard privacy. Data plays a vital role in corporate decision-making in the current information-centric landscape. Various sectors, like banking and healthcare, gather confidential information on a daily basis. This information is disseminated by multiple sources through numerous methods. Securing sensitive data is of paramount importance for any data mining application. This study safeguarded confidential information using an anonymization technique. Several machine learning methodologies have a deficiency in accuracy. The study seeks to generate superior and more precise results compared to alternative methodologies. For large datasets, numerous solutions exhibit increased time complexity and memory use. For huge datasets, numerous solutions require more time and memory. The enhanced fuzzy C-means (FCM) algorithm surpasses existing approaches in terms of both accuracy and information preservation. This study provides a comprehensive analysis of data anonymization utilizing the oppositional fruit fly approach, a technique that enhances privacy. The clustering method being presented utilizes an enhanced version of the FCM algorithm. The secrecy of the recommended oppositional fruit fly algorithm is effective. The comparison demonstrated that the proposed research enhanced both accuracy and privacy in comparison to two existing methods. The existing strategy outperforms data anonymization-based privacy preservation by 82.17%, while the suggested method surpasses it by 94.17%.

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.

Evaluating regional sustainable energy potential through hierarchical clustering and machine learning

Abstract Energy is an essential resource for sustaining daily life and achieving economic growth. The increase in global energy demand, combined with the adverse environmental impacts of fossil fuels, has highlighted the urgency of transitioning to sustainable energy sources. In large and heterogeneous countries like Türkiye, region-specific analyses of sustainable energy potential (SEP) are crucial for formulating effective policies and optimizing resource allocation. This study introduces a novel two-step hierarchical clustering and classification framework to evaluate the SEP of Türkiye’s provinces comprehensively. The framework combines fuzzy and crisp clustering methods to capture the complex relationships among socioeconomic, geographical, and renewable energy potential criteria. First, the Fuzzy C-Means (FCM) algorithm is employed to perform fuzzy clustering using three main criteria and three different distance metrics—Euclidean, Manhattan, and Minkowski—, resulting in 21 clustering scenarios. Membership degrees from the fuzzy clustering phase are then integrated into a new dataset, which undergoes crisp clustering using the K-Means algorithm. This approach provides both granular and definitive cluster structures, enabling a robust analysis of regional energy characteristics. To validate the clustering results, supervised classification methods—including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—are utilized, alongside ensemble models based on RF and XGBoost. The classification results are compared with traditional clustering evaluation indices, such as the Silhouette and Calinski-Harabasz indices, demonstrating the feasibility of using classification models to assess clustering accuracy. This study’s key contributions lie in integrating clustering and classification methods systematically and providing actionable insights into which renewable energy sources are most suitable for each cluster. By tailoring policy recommendations to the unique characteristics of each cluster, this framework not only corroborates existing findings in the literature but also extends them by offering a practical methodology for regional energy planning.

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.

A Dissimilarity Measure Powered Feature Weighted Fuzzy C-Means Algorithm for Gene Expression Data

A Dissimilarity Measure Powered Feature Weighted Fuzzy C-Means Algorithm for Gene Expression Data

The distance and entropy measures-based intuitionistic fuzzy C-means and similarity matrix clustering algorithms and their applications

The distance and entropy measures-based intuitionistic fuzzy C-means and similarity matrix clustering algorithms and their applications