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

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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1

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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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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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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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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patidar, Nitin, and Kushboo patidar. "DESIGN AND IMPLEMENTATION DATA CLASSIFICATION USING FUZZY C-MEANS BASED ON HYBRID CLUSTERING TECHNIQUE." COMPUSOFT: An International Journal of Advanced Computer Technology 07, no. 06 (2018): 2793–96. https://doi.org/10.5281/zenodo.14807541.

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The management and analysis of big data has been recognized as one of the majority significant promising requirements in recent years. This is because of the pure volume and growing complexity of data creature created or composed. Existing clustering algorithms cannot grip big data, and consequently, scalable solutions are essential. The experimental analysis will be accepted out to assess the practicability of the scalable Possibilistic Fuzzy CMeans (PFCM) clustering technique and partial Fuzzy C-Means (PFCM) clustering technique.. Two-stage and twophase fuzzy C-means (FCM) algorithms have be

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Sumarauw, Sylvia. "Fuzzy c-Means Clustering untuk Pengenalan Pola Studi kasus Data Saham." Jurnal Axioma : Jurnal Matematika dan Pembelajaran 7, no. 2 (2022): 97–106. http://dx.doi.org/10.56013/axi.v7i2.1395.

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Fuzzy Clustering is one of the five roles used by data mining experts to transform large amounts of data into useful information, and one method that is often and widely used is Fuzzy c-Means (FCM) Clustering. FCM is a data clustering technique where the existence of each data point in the cluster is based on the degree of membership. This study aims to see the pattern of data samples or data categories using FCM clustering. The analyzed data is stock data on Jakarta Stock Exchange (BEJ) in the Property and Real Estate sector (issuer group). The data mining processes comply Cross Industry Stan

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Li, Qiang, Jian Pei Zhang, and Guang Sheng Feng. "An Outlier Detection Method Based on Fuzzy C-Means Clustering." Key Engineering Materials 419-420 (October 2009): 165–68. http://dx.doi.org/10.4028/www.scientific.net/kem.419-420.165.

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Both fuzzy c-means (FCM) clustering and outlier detection are useful data mining techniques in real applications. In this paper, we show that the task of outlier detection could be achieved as by-product of fuzzy c-means clustering. The proposed strategy consists of two stages. The first stage consists of purely fuzzy c-means process, while the second stage identifies exceptional objects according to a novel metric based on the entropy of membership values. We provide experimental results to demonstrate the effectiveness of our technique.

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Antony, Jaya Mabel Rani, Srivenkateswaran C., Rajasekar M., and Arun M. "Fuzzy C-means clustering on rainfall flow optimization technique for medical data." International Journal of Artificial Intelligence (IJ-AI) 12, no. 1 (2023): 180–88. https://doi.org/10.11591/ijai.v12.i1.pp180-188.

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Due to various killing diseases in the world, medical data clustering is a very challenging and critical task to handle and to take the proper decision from multidimensional complex data in an effective manner. The most familiar and suitable speedy clustering algorithm is K-means than other traditional clustering approaches. But K-means is extra sensitive for initialization of clustering centroid and it can easily surround. Thus, there is a necessity for faster clustering with an effective optimum clustering centroid. Based on that, this research paper projected an optimization-based clusterin

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Bashir, Muhammad Adnan, G. Muhiuddin, Tabasam Rashid, and Muhammad Shoaib Sardar. "Multicriteria Ordered the Profile Clustering Algorithm Based on PROMETHEE and Fuzzy c-Means." Mathematical Problems in Engineering 2023 (May 9, 2023): 1–13. http://dx.doi.org/10.1155/2023/5268340.

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The purpose of multicriteria clustering is to locate groups of alternatives that have comparable qualities and have been examined across multiple criteria. An ordered profile clustering is a well-known problem, and the fuzzy c-means clustering (FCM) technique is one of the most broadly used in every field of life. At present, FCM is for the partitioning of information into numerous clusters which are still lacking priority relations. To address the problem of finding ranking in clusters based on multicriteria in the fuzzy environment, we propose a multicriteria ordered clustering algorithm bas

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Budiyanti, Meysi, and Mutia Nur Estri. "FUZZY C-MEANS CLUSTERING UNTUK PENGELOMPOKAN BAHAN MAKANAN BERDASARKAN KANDUNGAN ZAT GIZI." Jurnal Ilmiah Matematika dan Pendidikan Matematika 4, no. 1 (2012): 223. http://dx.doi.org/10.20884/1.jmp.2012.4.1.2958.

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Fuzzy C-Means Clustering (FCM) is a data clustering technique where each data point belongs to a cluster by membership degree. FCM starts with the concept of cluster centers that mark the mean location of each cluster. By iteratively updating the cluster centers and the membership degree for each data point, then the cluster centers move to the right location. FCM can be applied to group the nutrients of foods based on three functions of nutrients which are food as energy provider, body functions regulator and growth developer. Each nutrients are grouped into three cluster. The information of

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Alaba, AJALA Funmilola, AKANDE Noah Oluwatobi, ADEYEMO Isiaka Akinkunmi, and Ogundokun Roseline Oluwaseun. "Smallest Univalue Segment Assimilating Nucleus approach to Brain MRI Image Segmentation using Fuzzy C-Means and Fuzzy K-Means Algorithms." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 16, no. 7 (2017): 7065–76. http://dx.doi.org/10.24297/ijct.v16i7.6170.

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Image segmentation still remains an important task in image processing and analysis. Sequel to any segmentation process, preprocessing activities carried out on the images have a great effect on the accuracy of the segmentation task. This paper therefore laid emphasis on the preprocessing stage of brain Magnetic Resonance Imaging (MRI) images Smallest Univalue Segment Assimilating Nucleus (SUSAN) and bias field correction algorithms. Subsequently, brain tissue extraction tool was employed in extracting non-brain tissues from the brain image. Afterwards, Fuzzy K-Means (FKM) and Fuzzy C-Means (F

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Khang, Tran Dinh, Manh-Kien Tran, and Michael Fowler. "A Novel Semi-Supervised Fuzzy C-Means Clustering Algorithm Using Multiple Fuzzification Coefficients." Algorithms 14, no. 9 (2021): 258. http://dx.doi.org/10.3390/a14090258.

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Clustering is an unsupervised machine learning method with many practical applications that has gathered extensive research interest. It is a technique of dividing data elements into clusters such that elements in the same cluster are similar. Clustering belongs to the group of unsupervised machine learning techniques, meaning that there is no information about the labels of the elements. However, when knowledge of data points is known in advance, it will be beneficial to use a semi-supervised algorithm. Within many clustering techniques available, fuzzy C-means clustering (FCM) is a common on

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Mabel Rani, Antony Jaya, C. Srivenkateswaran, M. Rajasekar, and M. Arun. "Fuzzy C-means clustering on rainfall flow optimization technique for medical data." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 1 (2023): 180. http://dx.doi.org/10.11591/ijai.v12.i1.pp180-188.

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<span lang="EN-US">Due to various killing diseases in the world, medical data clustering is a very challenging and critical task to handle and to take the proper decision from multidimensional complex data in an effective manner. The most familiar and suitable speedy clustering algorithm is K-means than other traditional clustering approaches. But K-means is extra sensitive for initialization of clustering centroid and it can easily surround. Thus, there is a necessity for faster clustering with an effective optimum clustering centroid. Based on that, this research paper projected an opt

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Messakh, Gerald Claudio, Memi Nor Hayati, and Sifriyani Sifriyani. "Comparison K-Means and Fuzzy C-Means In Regencies/Cities Grouping Based on Educational Indicators." Jurnal Varian 7, no. 1 (2023): 99–114. http://dx.doi.org/10.30812/varian.v7i1.2879.

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Cluster analysis is an analysis that aims to classify data based on the similarity of specific characteristics.The methods used in this research are K-Means and Fuzzy C-Means (FCM). K-Means is a partitionbased non-hierarchical data grouping method. FCM is a clustering technique in which the existenceof each data is determined by the degree of membership. The purpose of this study is to classifyregencies/cities in Kalimantan based on education indicators in 2021 using K-Means and FCM and findout which method is better to use between K-Means and FCM based on the standard deviation ratio soit can

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Kusumadewi, Sri, Linda Rosita, and Elyza Gustri Wahyuni. "Performance of Fuzzy C-Means (FCM) and Fuzzy Subtractive Clustering (FSC) on Medical Data Imputation." ComTech: Computer, Mathematics and Engineering Applications 15, no. 1 (2024): 29–40. http://dx.doi.org/10.21512/comtech.v15i1.11002.

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Missing values or incomplete data are frequently encountered in medical records. These issues will be a serious problem if the data must be provided completely for analysis. The research aimed to prove the performance of the Fuzzy Subtractive Clustering (FSC) and Fuzzy C-Means (FCM) methods for solving imputation problems. Both methods were implemented using medical data. It had been conducted using K-Means as a crisp clustering approach for imputation. In the research, fuzzy clustering—a distinct methodology—was applied. The primary research contribution was the suggested fuzzy logic imputati

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Kunwar, Vineeta, A. Sai Sabitha, Tanupriya Choudhury, and Archit Aggarwal. "Chronic Kidney Disease Using Fuzzy C-Means Clustering Analysis." International Journal of Business Analytics 6, no. 3 (2019): 43–64. http://dx.doi.org/10.4018/ijban.2019070104.

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Medical industries are encountered with challenges like providing quality services to patients, correct diagnosis and effective treatments at reasonable cost. Data mining has become a necessity and provides solutions to many important and critical health related concerns. It is the process to mine knowledgeable information from voluminous medical data sets. It plays an essential role in improving medical decision making and helps to investigate trends in patient conditions which can be used by doctors for disease diagnosis. Clustering is an unsupervised learning technique that groups object wi

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Yan, Ming Xia. "Drilling Wear Recognition Based on Fuzzy C-Means Clustering Algorithm." Advanced Materials Research 538-541 (June 2012): 1408–12. http://dx.doi.org/10.4028/www.scientific.net/amr.538-541.1408.

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Fuzzy c-means clustering algorithm was introduced in detail to classify a set of original sampling data on drilling wear in this paper. Simulation results by Matlab programming show that drill wear modes can be successfully represented by four fuzzy grades after fuzzy clustering and classification. The analysis result indicates that fuzzy description can properly reflect drill wear, FCM can effectively identify different wear modes. It is suggested that the severe degree of membership of wear be used as a criterion for replacement of a drill. This technique is simple and is adaptable to differ

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Salama, Mostafa A., and Aboul Ella Hassanien. "Fuzzification of Euclidean Space Approach in Machine Learning Techniques." International Journal of Service Science, Management, Engineering, and Technology 5, no. 4 (2014): 29–43. http://dx.doi.org/10.4018/ijssmet.2014100103.

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Euclidian calculations represent a cornerstone in many machine learning techniques such as the Fuzzy C-Means (FCM) and Support Vector Machine (SVM) techniques. The FCM technique calculates the Euclidian distance between different data points, and the SVM technique calculates the dot product of two points in the Euclidian space. These calculations do not consider the degree of relevance of the selected features to the target class labels. This paper proposed a modification in the Euclidian space calculation for the FCM and SVM techniques based on the ranking of features extracted from evaluatin

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Assi, Khaled, Syed Masiur Rahman, Umer Mansoor, and Nedal Ratrout. "Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol." International Journal of Environmental Research and Public Health 17, no. 15 (2020): 5497. http://dx.doi.org/10.3390/ijerph17155497.

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Predicting crash injury severity is a crucial constituent of reducing the consequences of traffic crashes. This study developed machine learning (ML) models to predict crash injury severity using 15 crash-related parameters. Separate ML models for each cluster were obtained using fuzzy c-means, which enhanced the predicting capability. Finally, four ML models were developed: feed-forward neural networks (FNN), support vector machine (SVM), fuzzy C-means clustering based feed-forward neural network (FNN-FCM), and fuzzy c-means based support vector machine (SVM-FCM). Features that were easily id

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Biniaz, Abbas, and Ataollah Abbasi. "Fast FCM with Spatial Neighborhood Information for Brain Mr Image Segmentation." Journal of Artificial Intelligence and Soft Computing Research 3, no. 1 (2013): 15–25. http://dx.doi.org/10.2478/jaiscr-2014-0002.

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Abstract Among different segmentation approaches Fuzzy c-Means clustering (FCM) is a welldeveloped algorithm for medical image segmentation. In emergency medical applications quick convergence of FCM is necessary. On the other hand spatial information is seldom exploited in standard FCM; therefore nuisance factors can simply affect it and cause misclassification. This paper aims to introduce a Fast FCM (FFCM) technique by incorporation of spatial neighborhood information which is exploited by a linear function on fuzzy membership. Applying proposed spatial Fast FCM (sFFCM), elapsed time is dec

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Jai Shankar, B., K. Murugan, A. Obulesu, S. Finney Daniel Shadrach, and R. Anitha. "MRI Image Segmentation Using Bat Optimization Algorithm with Fuzzy C Means (BOA-FCM) Clustering." Journal of Medical Imaging and Health Informatics 11, no. 3 (2021): 661–66. http://dx.doi.org/10.1166/jmihi.2021.3365.

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Functional and anatomical information extraction from Magnetic Resonance Images (MRI) is important in medical image applications. The information extraction is highly influenced by the artifacts in the MRI images. The feature extraction involves the segmentation of MRI images. We present a MRI image segmentation using Bat Optimization Algorithm (BOA) with Fuzzy C Means (FCM) clustering. Echolocation of bats is utilized in Bat Optimization Algorithm. The proposed segmentation technique is evaluated with existing segmentation techniques. Results of experimentation shows that proposed segmentatio

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Chen, Min, and Simone A. Ludwig. "Color Image Segmentation Using Fuzzy C-Regression Model." Advances in Fuzzy Systems 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/4582948.

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Image segmentation is one important process in image analysis and computer vision and is a valuable tool that can be applied in fields of image processing, health care, remote sensing, and traffic image detection. Given the lack of prior knowledge of the ground truth, unsupervised learning techniques like clustering have been largely adopted. Fuzzy clustering has been widely studied and successfully applied in image segmentation. In situations such as limited spatial resolution, poor contrast, overlapping intensities, and noise and intensity inhomogeneities, fuzzy clustering can retain much mo

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Karaca, Yeliz, Carlo Cattani, Majaz Moonis, and Şengül Bayrak. "Stroke Subtype Clustering by Multifractal Bayesian Denoising with Fuzzy C Means and K-Means Algorithms." Complexity 2018 (2018): 1–15. http://dx.doi.org/10.1155/2018/9034647.

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Multifractal denoising techniques capture interest in biomedicine, economy, and signal and image processing. Regarding stroke data there are subtle details not easily detectable by eye physicians. For the stroke subtypes diagnosis, details are important due to including hidden information concerning the possible existence of medical history, laboratory results, and treatment details. Recently, K-means and fuzzy C means (FCM) algorithms have been applied in literature with many datasets. We present efficient clustering algorithms to eliminate irregularities for a given set of stroke dataset usi

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Kurniawan, Muchamad, Rani Rotul Muhima, and Siti Agustini. "Comparison of Clustering K-Means, Fuzzy C-Means, and Linkage for Nasa Active Fire Dataset." International Journal of Artificial Intelligence & Robotics (IJAIR) 2, no. 2 (2020): 34. http://dx.doi.org/10.25139/ijair.v2i2.3030.

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One of the causes of forest fires is the lack of speed of handling when a fire occurs. This can be anticipated by determining how many extinguishing units are in the center of the hot spot. To get hotspots, NASA has provided an active fire dataset. The clustering method is used to get the most optimal centroid point. The clustering methods we use are K-Means, Fuzzy C-Means (FCM), and Average Linkage. The reason for using K-means is a simple method and has been applied in various areas. FCM is a partition-based clustering algorithm which is a development of the K-means method. The hierarchical

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Hidayat, Syahroni, Ria Rismayati, Muhammad Tajuddin, and Ni Luh Putu Merawati. "Segmentation of university customers loyalty based on RFM analysis using fuzzy c-means clustering." Jurnal Teknologi dan Sistem Komputer 8, no. 2 (2020): 133–39. http://dx.doi.org/10.14710/jtsiskom.8.2.2020.133-139.

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One of the strategic plans of the developing universities in obtaining new students is forming a partnership with surrounding high schools. However, partnerships made does not always behave as expected. This paper presented the segmentation technique to the previous new student admission dataset using the integration of recency, frequency, and monetary (RFM) analysis and fuzzy c-means (FCM) algorithm to evaluate the loyalty of the entire school that has bound the partnership with the institution. The dataset is converted using the RFM approach before processed with the FCM algorithm. The resul

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Dhivya, R., and R. Prakash. "Edge Detection of Images Using Improved Fuzzy C-Means and Artificial Neural Network Technique." Journal of Medical Imaging and Health Informatics 9, no. 6 (2019): 1284–93. http://dx.doi.org/10.1166/jmihi.2019.2719.

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Edge detection (ED) is an embryonic development, which is essential for any intricate image processing and recognition undertaking. This paper proposed another system to upgrade the method and Artificial neural network for speaking to vulnerability in the image slopes and collection. The vulnerability in the image inclination distinguishes the genuine edges which might be overlooked by other systems. This e is valuable in the field of restorative imaging applications, for example, MRI division, cerebrum tumor, filtering and so on. Attractive reaction imaging connected in restorative science to

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Koundal, Ashima, Sumit Budhiraja, and Sunil Agrawal. "Medical Image Segmentation using Enhanced Feature Weight Learning Based FCM Clustering." Biomedical and Pharmacology Journal 17, no. 4 (2024): 2661–72. https://doi.org/10.13005/bpj/3056.

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Image segmentation is a way to simplify and analyze images by separating them into different segments. Fuzzy c-means (FCM) is the most widely used clustering algorithm, as it can handle data with blurry boundaries; where points belong to multiple clusters with varying strengths. The segmentation performance of this method is sensitive to the initial cluster centers. The fact that every feature in the image contributes equally and is given equal weight is another issue with this algorithm. In this paper, an image segmentation technique based on Fuzzy C-means (FCM) method is proposed. The propos

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Nematzadeh, Zahra, Roliana Ibrahim, Ali Selamat, and Vahdat Nazerian. "The synergistic combination of fuzzy C-means and ensemble filtering for class noise detection." Engineering Computations 37, no. 7 (2020): 2337–55. http://dx.doi.org/10.1108/ec-05-2019-0242.

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Purpose The purpose of this study is to enhance data quality and overall accuracy and improve certainty by reducing the negative impacts of the FCM algorithm while clustering real-world data and also decreasing the inherent noise in data sets. Design/methodology/approach The present study proposed a new effective model based on fuzzy C-means (FCM), ensemble filtering (ENS) and machine learning algorithms, called an FCM-ENS model. This model is mainly composed of three parts: noise detection, noise filtering and noise classification. Findings The performance of the proposed model was tested by

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Paithane, Pradip, and Sarita Jibhau Wagh. "Novel modified kernel fuzzy c-means algorithm used for cotton leaf spot detection." System research and information technologies, no. 4 (December 26, 2023): 85–99. http://dx.doi.org/10.20535/srit.2308-8893.2023.4.07.

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Image segmentation is a significant and difficult subject that is a prerequisite for both basic image analysis and sophisticated picture interpretation. In image analysis, picture segmentation is crucial. Several different applications, including those related to medicine, facial identification, Cotton disease diagnosis, and map object detection, benefit from image segmentation. In order to segment images, the clustering approach is used. The two types of clustering algorithms are Crisp and Fuzzy. Crisp clustering is superior to fuzzy clustering. Fuzzy clustering uses the well-known FCM approa

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Basukala, Dibash, Debesh Jha, and Goo-Rak Kwon. "Brain Image Segmentation Based on Dual-Tree Complex Wavelet Transform and Fuzzy C-Means Clustering Algorithm." Journal of Medical Imaging and Health Informatics 8, no. 9 (2018): 1776–81. http://dx.doi.org/10.1166/jmihi.2018.2524.

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Image segmentation is an important step in most medical image analysis tasks. An effective image segmentation method helps clinicians and patients in image-guided surgery, radiotherapy, early disease detection, volumetric measurement, and three-dimensional visualization. The fuzzy c-means (FCM) clustering algorithm is one of the most popular methods used for medical image segmentation. However, it does not produce satisfactory results for images with noise and intensity inhomogeneities. Hence, a wavelet-based FCM clustering algorithm is proposed in this work. An advanced wavelet transform, suc

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Rayala, Venkat, and Satyanarayan Reddy Kalli. "Big Data Clustering Using Improvised Fuzzy C-Means Clustering." Revue d'Intelligence Artificielle 34, no. 6 (2020): 701–8. http://dx.doi.org/10.18280/ria.340604.

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Clustering emerged as powerful mechanism to analyze the massive data generated by modern applications; the main aim of it is to categorize the data into clusters where objects are grouped into the particular category. However, there are various challenges while clustering the big data recently. Deep Learning has been powerful paradigm for big data analysis, this requires huge number of samples for training the model, which is time consuming and expensive. This can be avoided though fuzzy approach. In this research work, we design and develop an Improvised Fuzzy C-Means (IFCM)which comprises th

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Dharsni, Chandra. "KEMIRIPAN LIPSTIK BERDASARKAN METODE FUZZY C-MEANS (FCM) MENGGUNAKAN DELPHI." KLIK - KUMPULAN JURNAL ILMU KOMPUTER 7, no. 1 (2020): 48. http://dx.doi.org/10.20527/klik.v7i1.299.

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<p>Laser Induced Breakdown Spectroscopy (LIBS) is a spectroscopic method for quantitative and qualitative analysis of elements contained in a material. This technique is based on an analysis of plasma emissions produced by focusing a high-power pulse laser on a sample. However, to determine the similarity of the content of a material based on spectroscopy is difficult, especially for similar materials. Lipstick itself has many color variations and some colors look almost the same. To distinguish each color, lipstick manufacturers give numbers or names on this product. In this case the wr

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Mhadhbi, Imene, Slim Ben Othman, and Slim Ben Saoud. "An Efficient Technique for Hardware/Software Partitioning Process in Codesign." Scientific Programming 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/6382765.

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Codesign methodology deals with the problem of designing complex embedded systems, where automatic hardware/software partitioning is one key issue. The research efforts in this issue are focused on exploring new automatic partitioning methods which consider only binary or extended partitioning problems. The main contribution of this paper is to propose a hybrid FCMPSO partitioning technique, based on Fuzzy C-Means (FCM) and Particle Swarm Optimization (PSO) algorithms suitable for mapping embedded applications for both binary and multicores target architecture. Our FCMPSO optimization techniqu

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Sanmorino, Ahmad. "Clustering Batik Images using Fuzzy C-Means Algorithm Based on Log-Average Luminance." Computer Engineering and Applications Journal 1, no. 1 (2012): 25–31. http://dx.doi.org/10.18495/comengapp.v1i1.3.

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Batik is a fabric or clothes that are made â€‹â€‹with a special staining technique called wax-resist dyeing and is one of the cultural heritage which has high artistic value. In order to improve the efficiency and give better semantic to the image, some researchers apply clustering algorithm for managing images before they can be retrieved. Image clustering is a process of grouping images based on their similarity. In this paper we attempt to provide an alternative method of grouping batik image using fuzzy c-means (FCM) algorithm based on log-average luminance of the batik. FCM clustering alg

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Chuwdhury, Gulam Sarwar, Md Khaliluzzaman, and Md Rashed-Al Mahfuz. "Analyzing Wavelet and Bidimensional Empirical Mode Decomposition of MRI Segmentation using Fuzzy C-Means Clustering." Rajshahi University Journal of Science and Engineering 44 (November 19, 2016): 101–12. http://dx.doi.org/10.3329/rujse.v44i0.30395.

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Image segmentation is a vital step in medical image processing. Magnetic resonance imaging (MRI) is used for brain tissues extraction in white and gray matter. These tissues extraction help in image segmentation applications such as radiotherapy planning, clinical diagnosis, treatment planning. This paper presents utilization of fuzzy C-means (FCM) clustering by using wavelet and bidimensional empirical mode decomposition (BEMD) to improve the quality of noisy MR images. The signal to noise ratio (SNR) value is calculated from FCM clustering data to examine the best segmentation technique. The

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Liang, Haobo, Yuan Feng, Yushi Zhang, Xingshuai Qiao, Zhi Wang, and Tao Shan. "A Segmented Sliding Window Reference Signal Reconstruction Method Based on Fuzzy C-Means." Remote Sensing 16, no. 10 (2024): 1813. http://dx.doi.org/10.3390/rs16101813.

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Reference signal reconstruction serves as a crucial technique for suppressing multipath interference and noise in the reference channel of passive radar. Aiming at the challenge of detecting Low-Slow-Small (LSS) targets using Digital Terrestrial Multimedia Broadcasting (DTMB) signals, this article proposes a novel segmented sliding window reference signal reconstruction method based on Fuzzy C-Means (FCM). By partitioning the reference signals based on the structure of DTMB signal frames, this approach compensates for frequency offset and sample rate deviation individually for each segment. Ad

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Latifah, Ummu Wachidatul, Sugiyarto Surono, and Suparman Suparman. "K-means and fuzzy c-means algorithm comparison on regency/city grouping in Central Java Province." Desimal: Jurnal Matematika 5, no. 2 (2022): 155–68. http://dx.doi.org/10.24042/djm.v5i2.12204.

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The Human Development Index (HDI) is very important in measuring the country's success as an effort to build the quality of life of people in a region, including Indonesia. The government needs to make groupings based on the needs of a city/district. To facilitate data grouping based on the similarity of existing characteristics, it is necessary to have a data grouping method, namely the clustering technique. There are several algorithms that are often used in clustering techniques, namely K-Means and Fuzzy C-Means. Each algorithm has advantages and disadvantages. Therefore, in this research,

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Al Aqad, Mohammed H. "Ant Colony Optimization (ACO) Based Fuzzy C-Means (FCM) Clustering Approach for MRI Images Segmentation." Wasit Journal of Computer and Mathematics Science 2, no. 4 (2023): 115–25. http://dx.doi.org/10.31185/wjcms.230.

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Thousands of real-life ants have been used to improve the Ant Colony Optimization (ACO) technique. Significant improvement can be noticed in segmented images using ACO-based rather than random initialization. The segmentation quality has improved as a result of the noise reduction. Clustering based on FCM is used to segment medical images. To avoid local optimal results, cluster centres are initially determined using ACO. This paper shows that our approach (ACO-FCM) provides significant improvements. In ACO-FCM, brain tissues are classified more accurately because there are more correctly clas

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Liu, Bowen, Ting Zhang, Yujian Li, Zhaoying Liu, and Zhilin Zhang. "Kernel Probabilistic K-Means Clustering." Sensors 21, no. 5 (2021): 1892. http://dx.doi.org/10.3390/s21051892.

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Kernel fuzzy c-means (KFCM) is a significantly improved version of fuzzy c-means (FCM) for processing linearly inseparable datasets. However, for fuzzification parameter m=1, the problem of KFCM (kernel fuzzy c-means) cannot be solved by Lagrangian optimization. To solve this problem, an equivalent model, called kernel probabilistic k-means (KPKM), is proposed here. The novel model relates KFCM to kernel k-means (KKM) in a unified mathematic framework. Moreover, the proposed KPKM can be addressed by the active gradient projection (AGP) method, which is a nonlinear programming technique with co

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Jermyn, Jacqueline. "A Comparison of Fuzzy C-Means and K-Means Clustering for Extraction of City Colours." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (2023): 143–51. http://dx.doi.org/10.22214/ijraset.2023.48501.

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Abstract: The colour palette of each urban metropolis reflects its cultural identity and its unique flair. For urban development and urban renewal projects, incorporating a city’s existing colour palette into a construction plan would ensure that the completed project would be in harmony with the existing colour schemes of a neighbourhood. An earlier investigation implemented Fuzzy C-Means (FCM) colour extraction to identify five dominant colours from the images of each of the twelve major cosmopolitan cities that are situated on six continents. These cities were Cairo, Cape Town, Singapore, T

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Kumar, M. Sandeep, and Prabhu J. "Hybrid Model for Movie Recommendation System Using Fireflies and Fuzzy C-Means." International Journal of Web Portals 11, no. 2 (2019): 1–13. http://dx.doi.org/10.4018/ijwp.2019070101.

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In the era of Big Data, extremely complicated data is delivered from the system, of which it is impossible to collect the correct information with an online platform. In this research work, it provides a hybrid model for a movie-based recommender system; based on meta-heuristic firefly algorithm and fuzzy c-means (FCM) clustering technique to evaluate rating of a movie for a specific user based on the similarity of users and historical data. The firefly algorithm was employed in the movie lens dataset to get the initial cluster and also to initialize the position of clusters. FCM is used to cl

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Yanto, Iwan Tri Riyadi, Ririn Setiyowati, Nursyiva Irsalinda, Rasyidah, and Tri Lestari. "Laying Chicken Algorithm (LCA) Based For Clustering." JOIV : International Journal on Informatics Visualization 4, no. 4 (2020): 208. http://dx.doi.org/10.30630/joiv.4.4.467.

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Numerous research and related applications of fuzzy clustering are still interesting and important. In this paper, Fuzzy C-Means (FCM) and Laying Chicken Algorithm (LCA) were modified to improve local optimum of Fuzzy Clustering presented by using UCI dataset. In this study, the proposed FCMLCA performance was also compared to baseline technique based on CSO methods. The simulation results indicate that the FCMLCA method have better performance than the compared methods.

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Lei, Yang, and Minqing Zhang. "Technique for Kernel Matching Pursuit Based on Intuitionistic Fuzzy c-Means Clustering." Electronics 13, no. 14 (2024): 2777. http://dx.doi.org/10.3390/electronics13142777.

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Kernel matching pursuit (KMP) requires every step of the searching process to be global optimal searching in the redundant dictionary of functions in order to select the best matching signal structure. Namely, the dictionary learning time of KMP is too long. To solve the above drawbacks, a rough dataset was divided into some small-sized dictionaries to substitute local searching for global searching by using the property superiority of dynamic clustering performance, which is also superior in the intuitionistic fuzzy c-means (IFCM) algorithm. Then, we proposed a novel technique for KMP based o

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Manjeet, Kaur*1 &. Dr. Tryambak Hirwakar2. "DATA MINING FOR BIOLOGICAL PROBLEMS BY K-MEANS ALGORITHM." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 6, no. 6 (2019): 24–32. https://doi.org/10.5281/zenodo.3244246.

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Data Mining is a knowledge discovery from data and it treats as mining ofknowledge from large amount of data in every field. The algorithms are implemented using MATLAB and fuzzy logic tool box and results are evaluated based on performance parameter in both algorithms. After doing this research experiment results show that how k-means and fuzzy C means implemented on protein data set. In this research work we present the problem that show proteins are highly affiliated to each other.FCM allows one piece of data to belong to two or more clusters. Results based on different clusters in both alg

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