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Relevant bibliographies by topics / FCM-Fuzzy c-means Technique

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Academic literature on the topic 'FCM-Fuzzy c-means Technique'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "FCM-Fuzzy c-means Technique"

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Khang, Tran Dinh, Nguyen Duc Vuong, Manh-Kien Tran, and Michael Fowler. "Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients." Algorithms 13, no. 7 (2020): 158. http://dx.doi.org/10.3390/a13070158.

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Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have been made to increase clustering efficiency. These improvements focus on adjusting the membership representation of elements in the clusters, or on fuzzifying and defuzzifying techniques, as well as the distance function between elements. This study proposes a novel fuzzy cluster

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Kembaren, Ricky Crist Geoversam Imantara, Opim Salim Sitompul, and Sawaluddin Sawaluddin. "Analysis Clustering Using Normalized Cross Correlation In Fuzzy C-Means Clustering Algorithm." Sinkron 7, no. 4 (2022): 2262–71. http://dx.doi.org/10.33395/sinkron.v7i4.11666.

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Abstract: Fuzzy C-Means Clustering (FCM) has been widely known as a technique for performing data clustering, such as image segmentation. This study will conduct a trial using the Normalized Cross Correlation method on the Fuzzy C-Means Clustering algorithm in determining the value of the initial fuzzy pseudo-partition matrix which was previously carried out by a random process. Clustering technique is a process of grouping data which is included in unsupervised learning. Data mining generally has two techniques in performing clustering, namely: hierarchical clustering and partitional clusteri

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Parveen, Shazia, and Miin-Shen Yang. "Sparse Fuzzy C-Means Clustering with Lasso Penalty." Symmetry 16, no. 9 (2024): 1208. http://dx.doi.org/10.3390/sym16091208.

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Clustering is a technique of grouping data into a homogeneous structure according to the similarity or dissimilarity measures between objects. In clustering, the fuzzy c-means (FCM) algorithm is the best-known and most commonly used method and is a fuzzy extension of k-means in which FCM has been widely used in various fields. Although FCM is a good clustering algorithm, it only treats data points with feature components under equal importance and has drawbacks for handling high-dimensional data. The rapid development of social media and data acquisition techniques has led to advanced methods

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Du, Shi Ping, Jian Wang, and Yu Ming Wei. "The Training Algorithm of Fuzzy Coupled Hidden Markov Models." Applied Mechanics and Materials 568-570 (June 2014): 254–59. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.254.

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A variety of coupled hidden Markov models (CHMMs) have recently been proposed as extensions of HMM to better characterize multiple interdependent sequences. The resulting models have multiple state variables that are temporally coupled via matrices of conditional probabilities. A generalised fuzzy approach to statistical modelling techniques is proposed in this paper. Fuzzy C-means (FCM) and fuzzy entropy (FE) techniques are combined into a generalised fuzzy technique and applied to coupled hidden Markov models. The CHMM based on the fuzzy c-means (FCM) and fuzzy entropy (FE) is referred to as

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B., Sasi Prabha. "Fuzzy C-means Approach to Ovarian Cancer Recognition and Analysis." International Journal of Innovative Science and Research Technology (IJISRT) 9, no. 12 (2025): 3151–55. https://doi.org/10.5281/zenodo.14928740.

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Now a day’s image processing technique are very exigent and extensively used in charitable medical area for Ovarian cancer remains a significant health challenge due to its often-asymptomatic nature and late-stage diagnosis. Early detection and precise identification are crucial for improving patient outcomes. This paper presents a novel approach for ovarian cancer detection and identification through the application of Fuzzy C-Means (FCM) clustering, an advanced unsupervised machine learning technique. FCM clustering leverages fuzzy logic to handle uncertainty and variability in medical

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Sharma, Minakshi, and Saourabh Mukherjee. "Fuzzy C-Means, ANFIS and Genetic Algorithm for Segmenting Astrocytoma –A Type of Brain Tumor." IAES International Journal of Artificial Intelligence (IJ-AI) 3, no. 1 (2014): 16. http://dx.doi.org/10.11591/ijai.v3.i1.pp16-23.

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<p>Imaging plays an important role in medical field like medical diagnosis, treatment planning and patient follow up. Image segmentation is the backbone process to accomplish these tasks by dividing an image in to meaningful parts which share similar properties. Medical Resonance Imaging (MRI) is primary diagnostic technique to do image segmentation. There are several techniques proposed for image segmentation of different parts of body like Region growing, Thresholding, Clustering methods and Soft computing techniques (Fuzzy Logic, Neural Network, Genetic Algorithm).The proposed researc

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S., K. Srimonishaa* Dr. Muthukumar P. "Edge Detection Using Fuzzy C-Means: A Comparative Study." International Journal of Scientific Research and Technology 2, no. 3 (2025): 335–44. https://doi.org/10.5281/zenodo.15065735.

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Edge detection is a fundamental image processing technique that identifies boundaries within images by detecting abrupt intensity changes. This paper investigates the application of the Fuzzy C-Means (FCM) clustering algorithm in edge detection and compares its performance with traditional methods such as Sobel, Prewitt, and Canny. By leveraging MATLAB for implementation, the study highlights the advantages of FCM in handling overlapping data and its applicability in fields like medical imaging and computer vision. A specific focus is given to the edge detection of brain tumors in MRI images,

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Oktavianto, Hardian, Izzati Muhimmah, and Taufiq Hidayat. "SEGMENTASI AREA GIGI MENGGUNAKAN FUZZY C-MEANS." Jurnal Teknologi Informasi dan Terapan 4, no. 2 (2019): 75–82. http://dx.doi.org/10.25047/jtit.v4i2.63.

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Researches with early detection of caries using x-ray topic has been widely developed, generally before doing object detection, the early step is segmentation. Image segmentation is one of the digital image processing steps used to segregate an area or object observed with other areas or objects. Segmentation has an important role as the initial determination of the desired area or object so that it can be continued to the identification stage. FCM (fuzzy c-means) algorithm is one of object segmentation technique or object grouping in the field of digital image processing study. The basic conc

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Mallik, Moksud Alam, Hafsa Yasmeen, Nousheen Begum, Md Saiful Islam, and Sheik Jamil Ahmed. "Comparative Studies of Different Fuzzy-C-Means Clustering Algorithms for Machine Learning." International Research Journal of Innovations in Engineering and Technology 09, Special Issue (2025): 400–406. https://doi.org/10.47001/irjiet/2025.inspire65.

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A common machine learning technique for grouping data into clusters according to similarity is fuzzy C-Means (FCM) clustering, which permits each data point to belong to numerous clusters with differing degrees of membership. Because of its adaptability, FCM is a desirable option for applications including anomaly detection, pattern identification, and image segmentation. To overcome certain drawbacks including initialization sensitivity, computational cost, and managing data noise, several iterations and adaptations of the FCM algorithm have been put forth. In the context of machine learning

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DIKSHA, MALIK, KUMAR TARUN, KUMAR SHUBHAM, SHARMA AJENDRA, and K. SHARMA M. "MACHINE LEARNING-DRIVEN FUZZY C-MEANS CLUSTERING FOR MEDICAL IMAGE SEGMENTATION." Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) 42, no. 10 (2023): 164–84. https://doi.org/10.5281/zenodo.8429562.

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<strong>Abstract</strong> In the contemporary landscape of machine learning, medical image analysis has experienced monumental leaps forward. Spearheading this progression are cutting-edge clustering and segmentation methods having reshaping our analytical capabilities. This piece delves into the prowess of the Fuzzy C-Means (FCM) clustering technique: a machine learning-centric strategy. By scrutinizing five diverse case studies, we unravel the tangible benefits and the expansive potential of FCM. To ensure a comprehensive view, we also navigate through other prominent image segmentation meth

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Dissertations / Theses on the topic "FCM-Fuzzy c-means Technique"

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Naik, Vaibhav C. "Fuzzy C-means clustering approach to design a warehouse layout." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000437.

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Book chapters on the topic "FCM-Fuzzy c-means Technique"

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Hamouda, Kamal, Mohammed Mahfouz Elmogy, and B. S. El-Desouky. "A Fragile Watermarking Chaotic Authentication Scheme Based on Fuzzy C-Means for Image Tamper Detection." In Handbook of Research on Machine Learning Innovations and Trends. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2229-4.ch037.

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In the last two decades, several fragile watermarking schemes have been proposed for image authentication. In this paper, a novel fragile watermarking authentication scheme based on Chaotic Maps and Fuzzy C-Means (FCM) clustering technique is proposed. In order to raise the value of the tamper localization, detection accuracy, and security of the watermarking system the hybrid technique between Chaotic maps and FCM are introduced. In addition, this scheme can be applied to any image with different sizes not only in the square or even sized images. The proposed scheme gives high values especial

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Bhimavarapu, Usharani. "Optimizing Pedagogical Interventions and Advancing Student Performance Using Fuzzy C-Means Clustering." In Improving Academic Performance and Achievement With Inclusive Learning Practices. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-4501-7.ch004.

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Pedagogical interventions for enhancing students' academic performance need effective data-driven solutions to identify learning patterns and provide personalized assistance. In this study, an advanced Fuzzy C-Means (FCM) clustering technique is employed to group students based on academic performance, attendance, and learning behavior. Principal Component Analysis (PCA) is used for feature selection to maximize dimensionality reduction without losing crucial information. The improved FCM integrates adaptive membership functions and hybrid similarity measures to optimize clustering precision,

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Sarkar, Anasua, and Rajib Das. "Remote Sensing Image Classification Using Fuzzy-PSO Hybrid Approach." In Geospatial Intelligence. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8054-6.ch029.

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Pixel classification among overlapping land cover regions in remote sensing imagery is a challenging task. Detection of uncertainty and vagueness are always key features for classifying mixed pixels. This chapter proposes an approach for pixel classification using hybrid approach of Fuzzy C-Means and Particle Swarm Optimization methods. This new unsupervised algorithm is able to identify clusters utilizing particle swarm optimization based on fuzzy membership values. This approach addresses overlapping regions in remote sensing images by uncertainties using fuzzy set membership values. PSO is

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Sarkar, Anasua, and Rajib Das. "Remote Sensing Image Classification Using Fuzzy-PSO Hybrid Approach." In Advances in Computational Intelligence and Robotics. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8291-7.ch014.

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Pixel classification among overlapping land cover regions in remote sensing imagery is a challenging task. Detection of uncertainty and vagueness are always key features for classifying mixed pixels. This chapter proposes an approach for pixel classification using hybrid approach of Fuzzy C-Means and Particle Swarm Optimization methods. This new unsupervised algorithm is able to identify clusters utilizing particle swarm optimization based on fuzzy membership values. This approach addresses overlapping regions in remote sensing images by uncertainties using fuzzy set membership values. PSO is

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Deb, Daizy, Alex Khang, and Avijit Kumar Chaudhuri. "Fuzzy Thresholding-Based Brain Image Segmentation Using Multi-Threshold Level Set Model." In Advances in Medical Diagnosis, Treatment, and Care. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-3679-3.ch007.

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Region of interest with reference to medical image is a challenging task. Clustering or grouping data objects can be used to isolate certain area of interest called image segmentation from human brain MRI scans is considered here. Together with the combination of Multilevel Otsu's thresholding and Level set approach, the most widely used fuzzy-based clustering like fuzzy C means (FCM) thresholding are taken into consideration. Here, the proper thresholding is determined using FCM thresholding. This threshold value can also be used to modify the Multilevel Otsu' method's threshold. The level se

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Pushkala V and S. Nirmala Devi. "Detection of Ketosis in Bovine Using Machine Learning Techniques." In Advances in Parallel Computing. IOS Press, 2021. http://dx.doi.org/10.3233/apc210054.

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This paper presents the different unsupervised machine learning algorithms used for Ketosis detection, based on the color characteristics taken from the Ketocheck rapid colorimetric test. The level of ketone bodies in bovine’s urine is represented by three color categories, range of dark green (right ketone level), green (normal range ketone level) and yellow/orange (higher ketone level). The color image is converted into HSV color space for better color discrimination. The proposed technique enables detection of ketosis by clustering every pixel in the image using unsupervised K-means cluster

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Demircan Keskin, Fatma. "Multi-Criteria Decision Making With Machine Learning for Vehicle Routing Problem." In Advances in Logistics, Operations, and Management Science. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8040-0.ch011.

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This chapter addresses vehicle routing problem with time windows (VRPTW), one of the most well-known combinatorial optimization problems with many real-world applications in the transportation sector. This chapter proposes a three-stage approach for VRPTW and presents an application of this approach to a real-life problem. The stages of the approach include clustering of customers, determining feasible routes and their criteria values for each cluster, and selecting the best routes for each cluster based on multi-criteria decision analysis. In the first stage of the proposed approach, a fuzzy

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S, Siddesha, S. K. Niranjan, and V. N. Manjunath Aradhya. "A Study of Different Color Segmentation Techniques for Crop Bunch in Arecanut." In Environmental and Agricultural Informatics. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9621-9.ch048.

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Arecanut is an important cash crop of India and ranks first in the production. Arecanut crop bunch segmentation plays very vital role in the process of harvesting. Work on arecanut crop bunch segmentation is of first kind in the literature and this chapter mainly focuses on exploring different color segmentation techniques such as Thresholding, K-means clustering, Fuzzy C Means (FCM), Fast Fuzzy C Means clustering (FFCM), Watershed and Maximum Similarity based Region Merging (MSRM). The effectiveness of the segmentation methods are evaluated on our own collection of Arecanut image dataset of s

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S, Siddesha, S. K. Niranjan, and V. N. Manjunath Aradhya. "A Study of Different Color Segmentation Techniques for Crop Bunch in Arecanut." In Advances in Computational Intelligence and Robotics. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9474-3.ch001.

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Arecanut is an important cash crop of India and ranks first in the production. Arecanut crop bunch segmentation plays very vital role in the process of harvesting. Work on arecanut crop bunch segmentation is of first kind in the literature and this chapter mainly focuses on exploring different color segmentation techniques such as Thresholding, K-means clustering, Fuzzy C Means (FCM), Fast Fuzzy C Means clustering (FFCM), Watershed and Maximum Similarity based Region Merging (MSRM). The effectiveness of the segmentation methods are evaluated on our own collection of Arecanut image dataset of s

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Velmurugan, Thambusamy, and K. Emayavaramban. "Performance Analysis of K-Means and Fuzzy C-Means (FCM) Clustering Algorithms for Diabetic Dataset." In Advances in Transdisciplinary Engineering. IOS Press, 2025. https://doi.org/10.3233/atde241346.

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In the contemporary era, with its incidence rising at an alarming rate, diabetes has become a major global health concern. This work presents a data mining approach that compares the K-Means and Fuzzy C-Means (FCM) clustering algorithms to answer the increasing demand for accurate diabetes management and prediction in a wide range of datasets. As data mining is essential for drawing insightful conclusions from large, complicated datasets, the study concentrates on using these methods to improve the precision and effectiveness of diabetes prediction models. The diabetes dataset is divided into

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Conference papers on the topic "FCM-Fuzzy c-means Technique"

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Adeleke, Oluwatobi, and Tien-Chien Jen. "Prediction of Electrical Energy Consumption in University Campus Residence Using FCM-Clustered Neuro-Fuzzy Model." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-96793.

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Abstract Developing a viable data-driven policy for the management of electrical-energy consumption in campus residences is contingent on the proper knowledge of the electricity usage pattern and its predictability. In this study, an adaptive neuro-fuzzy inference systems (ANFIS) was developed to model the electrical energy consumption of students’ residence using the University of Johannesburg, South Africa as a case study. The model was developed based on the environmental conditions vis-à-vis meteorological parameters namely temperature, wind speed, and humidity of the respective days as th

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Avila, Gabriela, and Arturo Pacheco-Vega. "An Assessment on the FCM Classification of Thermodynamic-Property Data." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-66421.

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In the present study we consider the algorithmic classification of thermodynamic properties of fluids using the fuzzy C-means (FCM) clustering methodology. The FCM is a technique that can find patters directly from the data. It is based on the minimization of an objective function that provides a measure of the dissimilarity of the data being classified in a particular group. The dissimilarity in the data is commonly formulated in terms of the Euclidean distance between the data points and the cluster centroids. This mathematical formulation and the efficient implementation are among its advan

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Yu, Deqi, and Ming Li. "Optimization Approach for Wind Farm Cable Layout Based on Iterative Self-Organizing Data Analysis Technique Algorithm and Swap Sequence Based Particle Swarm Algorithms." In ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-83860.

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Abstract In the fast evolving and increasingly competitive wind energy market, to minimize initial capex and maximize production profits are essential when designing a wind farm. The electrical cable accounts for around 10% of the total capex, reasonable layout of cable connections might induce significant reduction of electrical and construction cost. In this paper, the Fuzzy C-means (FCM) clustering algorithm is adopted to determine the location of the substation(s) and the ISODATA (Iterative Self-Organizing Data Analysis Technique Algorithm) is employed to divide the wind farm into finite s

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Guerra, Rodolfo E. Haber, Rodolfo Haber Haber, Angel Alique, Clodeinir R. Peres, and Salvador Ros. "Fuzzy Modeling on the Basis of FCM Technique: A Case Study Aiming at Process Supervision." In ASME 2001 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/imece2001/dsc-24589.

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Abstract The nonlinear behavior and complexity of machining processes have motivated researchers to use fuzzy model to effect process supervision. The main idea of this paper concerns the application of fuzzy logic and clustering techniques to develop a fuzzy model of the milling process aiming at the optimization of machine-tool performance and the overall machining process. A brief description of the algorithm employed is given, focused on the fuzzy c-mean technique (FCM). The results indicate that the FCM criterion is suitable for modeling complex processes such as the milling process. The

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Saha, Ratna, Mariusz Bajger, and Gobert Lee. "Spatial Shape Constrained Fuzzy C-Means (FCM) Clustering for Nucleus Segmentation in Pap Smear Images." In 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2016. http://dx.doi.org/10.1109/dicta.2016.7797086.

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Yiakopoulos, Christos, Konstantinos Gryllias, and Ioannis Antoniadis. "Rolling Element Bearing Fault Classification Using K-Means Frequency Domain Based Clustering." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87369.

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Condition monitoring is becoming increasingly important in industry due to the need of increased reliability and decreased loss of production caused by machine breakdown. However, most techniques presently available require a good level of expertise in order to apply them successfully. Therefore, there is a demand for techniques that can make decisions on the running health of a machine automatically and without the need for a specialist to examine the data and diagnose the problem. Artificial neural networks (ANN), fuzzy c-means (FCM), hierchical and partitional clustering, and support vector

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