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Journal articles on the topic 'Enhanced k-means and fuzzy c-mean algorithms'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Hetangi, D. Mehta* Daxa Vekariya Pratixa Badelia. "COMPARISON AND EVALUATION OF CLUSTER BASED IMAGE SEGMENTATION TECHNIQUES." Global Journal of Engineering Science and Research Management 4, no. 12 (2017): 24–33. https://doi.org/10.5281/zenodo.1098696.

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Image segmentation is the classification of an image into different groups. Numerous algorithms using different approaches have been proposed for image segmentation. A major challenge in segmentation evaluation comes from the fundamental conflict between generality and objectivity. A review is done on different types of clustering methods used for image segmentation. Also a methodology is proposed to classify and quantify different clustering algorithms based on their consistency in different applications. There are different methods and one of the most popular methods is k-means clustering al

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Roy Efendi Subarja and Billy Hendrik. "Evaluasi Performa Deteksi Penyakit Diabetes Dengan Fuzzy C-Means Dan K-Means Clustering." Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK ) 1, no. 3 (2023): 100–108. http://dx.doi.org/10.59061/jentik.v1i3.376.

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The increasing prevalence of diabetes has led to a growing need for accurate and efficient disease detection methods. This research focuses on evaluating the performance of diabetes detection using Fuzzy C-Means and K-Means clustering algorithms. The study aims to compare the effectiveness of these two clustering techniques in identifying diabetes cases based on relevant medical data. A dataset comprising various health parameters and diagnostic indicators was utilized for experimentation. The Fuzzy C-Means and K-Means algorithms were implemented to cluster the dataset, and their detection per

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Abdul Nasir, Aimi Salihah, Haryati Jaafar, Wan Azani Wan Mustafa, and Zeehaida Mohamed. "The Cascaded Enhanced k-Means and Fuzzy c-Means Clustering Algorithms for Automated Segmentation of Malaria Parasites." MATEC Web of Conferences 150 (2018): 06037. http://dx.doi.org/10.1051/matecconf/201815006037.

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Malaria continues to be one of the leading causes of death in the world, despite the massive efforts put forth by World Health Organization (WHO) in eradicating it, worldwide. Efficient control and proper treatment of this disease requires early detection and accurate diagnosis due to the large number of cases reported yearly. To achieve this aim, this paper proposes a malaria parasite segmentation approach via cascaded clustering algorithms to automate the malaria diagnosis process. The comparisons among the cascaded clustering algorithms have been made by considering the accuracy, sensitivit

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Aljebory, Karim Mohammed, Thabit Sultan Mohammed, and Mohammed U. Zainal. "Enhanced Image Segmentation: Merging Fuzzy K-Means and Fuzzy C-Means Clustering Algorithms for Medical Applications." Computer Science and Information Technology 9, no. 1 (2021): 1–13. http://dx.doi.org/10.13189/csit.2021.090101.

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Lu, Wei Jia, and Zhuang Zhi Yan. "Improved FCM Algorithm Based on K-Means and Granular Computing." Journal of Intelligent Systems 24, no. 2 (2015): 215–22. http://dx.doi.org/10.1515/jisys-2014-0119.

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AbstractThe fuzzy clustering algorithm has been widely used in the research area and production and life. However, the conventional fuzzy algorithms have a disadvantage of high computational complexity. This article proposes an improved fuzzy C-means (FCM) algorithm based on K-means and principle of granularity. This algorithm is aiming at solving the problems of optimal number of clusters and sensitivity to the data initialization in the conventional FCM methods. The initialization stage of the K-medoid cluster, which is different from others, has a strong representation and is capable of det

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Lubis, Andre Hasudungan, and Elysa Ramayana. "A Review on Appropriateness of Partitional Clustering Algorithms in Handling Transactional Data." International Journal of Research and Review 10, no. 9 (2023): 162–69. http://dx.doi.org/10.52403/ijrr.20230918.

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Clustering is an unsupervised learning that widely used in vast researches area. The technique also utilized in any disciplines that involves multivariate data analysis. In term of transactional data handling, the partitional clustering is promoted as the one method to explore knowledge from several attributes that are related the business. In this paper, we investigate the use of partitional clustering algorithms including k-means, k-medoids, Fuzzy C Means, CLARA, and CLARANS. The present article delineates the various stages that are integral to accomplishing a review. These stages encompass

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Yang, Yao, Chengmao Wu, Yawen Li, and Shaoyu Zhang. "Robust Semisupervised Kernelized Fuzzy Local Information C-Means Clustering for Image Segmentation." Mathematical Problems in Engineering 2020 (March 23, 2020): 1–22. http://dx.doi.org/10.1155/2020/5648206.

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To improve the effectiveness and robustness of the existing semisupervised fuzzy clustering for segmenting image corrupted by noise, a kernel space semisupervised fuzzy C-means clustering segmentation algorithm combining utilizing neighborhood spatial gray information with fuzzy membership information is proposed in this paper. The mean intensity information of neighborhood window is embedded into the objective function of the existing semisupervised fuzzy C-means clustering, and the Lagrange multiplier method is used to obtain its iterative expression corresponding to the iterative solution o

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Madhu, Anjali, Anil Kumar, and Peng Jia. "Exploring Fuzzy Local Spatial Information Algorithms for Remote Sensing Image Classification." Remote Sensing 13, no. 20 (2021): 4163. http://dx.doi.org/10.3390/rs13204163.

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Fuzzy c-means (FCM) and possibilistic c-means (PCM) are two commonly used fuzzy clustering algorithms for extracting land use land cover (LULC) information from satellite images. However, these algorithms use only spectral or grey-level information of pixels for clustering and ignore their spatial correlation. Different variants of the FCM algorithm have emerged recently that utilize local spatial information in addition to spectral information for clustering. Such algorithms are seen to generate clustering outputs that are more enhanced than the classical spectral-based FCM algorithm. Nonethe

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Mohamed, Hassan, Kazuo Nadaoka, and Takashi Nakamura. "Automatic Semantic Segmentation of Benthic Habitats Using Images from Towed Underwater Camera in a Complex Shallow Water Environment." Remote Sensing 14, no. 8 (2022): 1818. http://dx.doi.org/10.3390/rs14081818.

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Underwater image segmentation is useful for benthic habitat mapping and monitoring; however, manual annotation is time-consuming and tedious. We propose automated segmentation of benthic habitats using unsupervised semantic algorithms. Four such algorithms––Fast and Robust Fuzzy C-Means (FR), Superpixel-Based Fast Fuzzy C-Means (FF), Otsu clustering (OS), and K-means segmentation (KM)––were tested for accuracy for segmentation. Further, YCbCr and the Commission Internationale de l’Éclairage (CIE) LAB color spaces were evaluated to correct variations in image illumination and shadow effects. Be

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T Zalizam, T. Muda, Abdul Salam Rosalina, and Ismail Suzilah. "Adaptive Hybrid Blood Cell Image Segmentation." MATEC Web of Conferences 255 (2019): 01001. http://dx.doi.org/10.1051/matecconf/201925501001.

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Image segmentation is an important phase in the image recognition system. In medical imaging such as blood cell analysis, it becomes a crucial step in quantitative cytophotometry. Currently, blood cell images become predominantly valuable in medical diagnostics tools. In this paper, we present an adaptive hybrid analysis based on selected segmentation algorithms. Three designates common approaches, that are Fuzzy c-means, K-means and Mean-shift are adapted. Blood cell images that are infected with malaria parasites at various stages were tested. The most suitable method will be selected based

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Jiao, Runhai, Shaolong Liu, Wu Wen, and Biying Lin. "Incremental kernel fuzzy c-means with optimizing cluster center initialization and delivery." Kybernetes 45, no. 8 (2016): 1273–91. http://dx.doi.org/10.1108/k-08-2015-0209.

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Purpose The large volume of big data makes it impractical for traditional clustering algorithms which are usually designed for entire data set. The purpose of this paper is to focus on incremental clustering which divides data into series of data chunks and only a small amount of data need to be clustered at each time. Few researches on incremental clustering algorithm address the problem of optimizing cluster center initialization for each data chunk and selecting multiple passing points for each cluster. Design/methodology/approach Through optimizing initial cluster centers, quality of clust

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Fadil, Yousra Ahmed, Baidaa Al-Bander, and Hussein Y. Radhi. "Enhancement of medical images using fuzzy logic." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (2021): 1478–84. https://doi.org/10.11591/ijeecs.v23.i3.pp1478-1484.

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Image enhancement is one of the most critical subjects in computer vision and image processing fields. It can be considered as means to enrich the perception of images for human viewers. All kinds of images typically suffer from different problems such as weak contrast and noise. The primary purpose of image enhancement is to change an image's visual appearance. Many algorithms have recently been proposed for enhancing medical images. Image enhancement is still deemed a challenging task. In this paper, the fuzzy cmeans clustering (FCM) technique is utilized to enhance the medical images. T

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Fouad, Khaled M., Mahmoud M. Ismail, Ahmad Taher Azar, and Mona M. Arafa. "Advanced methods for missing values imputation based on similarity learning." PeerJ Computer Science 7 (July 21, 2021): e619. http://dx.doi.org/10.7717/peerj-cs.619.

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The real-world data analysis and processing using data mining techniques often are facing observations that contain missing values. The main challenge of mining datasets is the existence of missing values. The missing values in a dataset should be imputed using the imputation method to improve the data mining methods’ accuracy and performance. There are existing techniques that use k-nearest neighbors algorithm for imputing the missing values but determining the appropriate k value can be a challenging task. There are other existing imputation techniques that are based on hard clustering algor

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Fadil, Yousra Ahmed, Baidaa Al-Bander, and Hussein Y. Radhi. "Enhancement of medical images using fuzzy logic." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (2021): 1478. http://dx.doi.org/10.11591/ijeecs.v23.i3.pp1478-1484.

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Image enhancement is one of the most critical subjects in computer vision and image processing fields. It can be considered as means to enrich the perception of images for human viewers. All kinds of images typically suffer from different problems such as weak contrast and noise. The primary purpose of image enhancement is to change an image's visual appearance. Many algorithms have recently been proposed for enhancing medical images. Image enhancement is still deemed a challenging task. In this paper, the fuzzy c-means clustering (FCM) technique is utilized to enhance the medical images. The

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Velmani,, Dr Ramasamy, and Dr Arun Prasath Raveendran. "Brain Tumor Detection using Hybrid Clustering with Estimate Arguing." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 1 (2021): 403–10. http://dx.doi.org/10.61841/turcomat.v11i1.11559.

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Detection of tissues from MR brain images is quite difficult task in medical field applications. Segmentation is utilized to detect the tissues accurately. Many algorithms have been presented to detect the tissues from the MR brain images. Most of them were remained failure due to their inaccurate results. To resolve this problem, an analysis of tissues detection in MR images using hybridclustering with estimate arguing (HC-EA) is proposed. Our proposed methodology consists of pre-processing, tissues detection and calculating the estimated area of clustered tissues. Extensive simulated analysi

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Maina Mwangi, Peter, John Gichuki Ndia, and Geoffrey Muchiri Muketha. "AN EXTENDED K-MEANS CLUSTER HEAD SELECTION ALGORITHM FOR EFFICIENT ENERGY CONSUMPTION IN WIRELESS SENSOR NETWORKS." International Journal of Network Security & Its Applications 15, no. 03 (2023): 65–83. http://dx.doi.org/10.5121/ijnsa.2023.15305.

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Effective use of sensor nodes’ batteries in wireless sensor networks is critical since the batteries are difficult to recharge or replace. This is closely connected to the networks’ lifespan since once the battery is used up, the node is no longer useful. The entire network will not function if 60 to 80% of the nodes in it have completely depleted their energy. In order to minimize energy usage and sustain the network for a long time, many cluster head selection algorithms have been developed. However, the existing cluster head selection algorithms such as K-Means, particle swarm selection opt

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Peter, Maina Mwangi, Gichuki Ndia John, and Muchiri Muketha Geoffrey. "AN EXTENDED K-MEANS CLUSTER HEAD SELECTION ALGORITHM FOR EFFICIENT ENERGY CONSUMPTION IN WIRELESS SENSOR NETWORKS." International Journal of Network Security & Its Applications (IJNSA) 15, no. 3 (2023): 65–83. https://doi.org/10.5281/zenodo.8072861.

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Effective use of sensor nodes’ batteries in wireless sensor networks is critical since the batteries are difficult to recharge or replace. This is closely connected to the networks’ lifespan since once the battery is used up, the node is no longer useful. The entire network will not function if 60 to 80% of the nodes in it have completely depleted their energy. In order to minimize energy usage and sustain the network for a long time, many cluster head selection algorithms have been developed. However, the existing cluster head selection algorithms such as K-Means, particle swarm s

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Das, Souhardya, Proma Mondal, Shambhab Chaki, Pratyusha Rakshit, and Archana Chowdhury. "EHR Innovations: Shedding Light on Anemia in the Healthcare Paradigm." Advances in Artificial Intelligence and Machine Learning 04, no. 03 (2024): 2648–64. http://dx.doi.org/10.54364/aaiml.2024.43154.

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This study introduces a novel approach to Electronic Health Record (EHR) analysis, extending the use of phenotyping with machine learning (ML) models to enhance the recognition and treatment of anemia. It first examines the healthcare scenario in India and suggests potential improvements through data-driven personalized care. Using the MIMIC-III dataset, the research involves extensive data preprocessing and analysis to uncover key insights into anemia’s prevalence, gender distribution, comorbidities, and Intensive Care Unit (ICU) stays. Partitioning clustering algorithms like K-Means, K-medoi

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Chan, Si-Wa, Wei-Hsuan Hu, Yen-Chieh Ouyang, et al. "Quantitative Measurement of Breast Tumors Using Intravoxel Incoherent Motion (IVIM) MR Images." Journal of Personalized Medicine 11, no. 7 (2021): 656. http://dx.doi.org/10.3390/jpm11070656.

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Breast magnetic resonance imaging (MRI) is currently a widely used clinical examination tool. Recently, MR diffusion-related technologies, such as intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI), have been extensively studied by breast cancer researchers and gradually adopted in clinical practice. In this study, we explored automatic tumor detection by IVIM-DWI. We considered the acquired IVIM-DWI data as a hyperspectral image cube and used a well-known hyperspectral subpixel target detection technique: constrained energy minimization (CEM). Two extended CEM methods—kernel C

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Abdul-Nasir, Aimi Salihah, Mohd Yusoff Mashor, and Zeehaida Mohamed. "Segmentation of Malaria Parasite Based on Stained Blood Cells Detection." Journal of Biomimetics, Biomaterials and Biomedical Engineering 24 (July 2015): 43–55. http://dx.doi.org/10.4028/www.scientific.net/jbbbe.24.43.

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Malaria is characterized by its life-threatening and destructive capability through the cause of widespread sufferings, contributing to the increase in mortality rates throughout the various parts of the world. Since the needs for immediate and appropriate diagnosis of malaria are urgently needed, this paper proposes a procedure for colour image segmentation that has been utilized using the malaria images of P. vivax species. First, the malaria images are enhanced by using modified global contrast stretching technique. Then, cascaded moving k-means and fuzzy c-means clustering is applied in or

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Ukandu, Obumeneme, and Olamide Kalesanwo. "Leveraging Artificial Intelligence for Customer Segmentation and Demand Forecasting in the Car Rental Industry." Asian Journal of Research in Computer Science 18, no. 4 (2025): 452–72. https://doi.org/10.9734/ajrcos/2025/v18i4631.

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The dynamic car rental industry faces significant challenges in demand forecasting, with about 50% of companies reporting inaccuracies that result in fleet utilization rates of only 70-75% instead of the optimal 85-90%. The study integrates customer segmentation and demand forecasting into a framework using various ML models. This study utilized historical rental data from Secured Wheels Car Rental reports in Lagos and Ibadan, Nigeria. The data underwent thorough preprocessing, including cleaning, selecting relevant features, and splitting it for analysis. The study employs decision trees, ran

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K. Vijetha. "Enhanced Deep Learning Methodology for Detection and Identification of Brain Tumor using CNN." Advances in Nonlinear Variational Inequalities 28, no. 1s (2024): 33–43. http://dx.doi.org/10.52783/anvi.v28.2166.

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Introduction: Brain tumours are a significant global public health issue that require precise and effective diagnosis techniques for well-thought-out treatment strategies. Three separate works for the automatic identification and categorization of brain tumours in magnetic resonance imaging (MR) datasets are presented in this dissertation. Objectives: The primary objective of this work was to explore uses of the version of the discrete orthonormal S-transform (DOST) to extract texture characteristics and fuzzy C-means (FCM) for image segmentation. By using Linear Discriminant Analysis (LDA) an

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Wang, Hengda, Mohamad Farhan Mohamad Mohsin, and Muhammad Syafiq Mohd Pozi. "Beta Distribution Weighted Fuzzy C-Ordered-Means Clustering." Journal of Information and Communication Technology 23, no. 3 (2024): 523–59. http://dx.doi.org/10.32890/jict2024.23.3.6.

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The fuzzy C-ordered-means clustering (FCOM) is a fuzzy clustering algorithm that enhances robustness and clustering accuracy through the ordered mechanism based on fuzzy C-means (FCM). However, despite these improvements, the FCOM algorithm’s effectiveness remains unsatisfactory due to the significant time cost incurred by its ordered operation. To address this problem, an investigation was conducted on the ordered weighted model of the FCOM algorithm leading to proposed enhancements by introducing the beta distribution weighted fuzzy C-ordered-means clustering (BDFCOM). The BDFCOM algorithm u

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KANNAN, S. R., S. RAMATHILAGAM, R. DEVI, and YUEH-MIN HUANG. "ENTROPY TOLERANT FUZZY C-MEANS IN MEDICAL IMAGES." Journal of Innovative Optical Health Sciences 04, no. 04 (2011): 447–62. http://dx.doi.org/10.1142/s179354581100168x.

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Segmenting the Dynamic Contrast-Enhanced Breast Magnetic Resonance Images (DCE-BMRI) is an extremely important task to diagnose the disease because it has the highest specificity when acquired with high temporal and spatial resolution and is also corrupted by heavy noise, outliers, and other imaging artifacts. In this paper, we intend to develop efficient robust segmentation algorithms based on fuzzy clustering approach for segmenting the DCE-BMRs. Our proposed segmentation algorithms have been amalgamated with effective kernel-induced distance measure on standard fuzzy c-means algorithm along

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Bashir, Muhammad Adnan, Tabasam Rashid, and Muhammad Salman Bashir. "Generalized Ordered Intuitionistic Fuzzy C-Means Clustering Algorithm Based on PROMETHEE and Intuitionistic Fuzzy C-Means." International Journal of Intelligent Systems 2023 (September 22, 2023): 1–21. http://dx.doi.org/10.1155/2023/6686446.

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The problem of ordered clustering in the context of decision-making with multiple criteria has garnered significant interest from researchers in the field of management science and operational research. In real-world scenarios, the datasets often exhibit imprecision or uncertainty, which can lead to suboptimal ordered-clustering outcomes. However, the intuitionistic fuzzy c-means (IFCM) clustering algorithm enhances the accuracy and effectiveness of decision-making processes by effectively handling uncertain dataset information for clustering. Therefore, we propose a new clustering algorithm,

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Hu, Han, Changming Wang, Zhu Liang, Ruiyuan Gao, and Bailong Li. "Exploring Complementary Models Consisting of Machine Learning Algorithms for Landslide Susceptibility Mapping." ISPRS International Journal of Geo-Information 10, no. 10 (2021): 639. http://dx.doi.org/10.3390/ijgi10100639.

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Landslides frequently occur because of natural or human factors. Landslides cause huge losses to the economy as well as human beings every year around the globe. Landslide susceptibility prediction (LSP) plays a key role in the prevention of landslides and has been under investigation for years. Although new machine learning algorithms have achieved excellent performance in terms of prediction accuracy, a sufficient quantity of training samples is essential. In contrast, it is hard to obtain enough landslide samples in most the areas, especially for the county-level area. The present study aim

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Han, Jin-Woo, Sung-Hae Jun, and Kyung-Whan Oh. "Cluster Merging Using Enhanced Density based Fuzzy C-Means Clustering Algorithm." Journal of Korean Institute of Intelligent Systems 14, no. 5 (2004): 517–24. http://dx.doi.org/10.5391/jkiis.2004.14.5.517.

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Wu, Chengmao, and Siyu Zhou. "Robust Harmonic Fuzzy Partition Local Information C-Means Clustering for Image Segmentation." Symmetry 16, no. 10 (2024): 1370. http://dx.doi.org/10.3390/sym16101370.

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Considering the shortcomings of Ruspini partition-based fuzzy clustering in revealing the intrinsic correlation between different classes, a series of harmonic fuzzy local information C-means clustering for noisy image segmentation are proposed. Firstly, aiming at the shortage of Zadeh’s fuzzy sets, a new concept of generalized harmonic fuzzy sets is originally introduced and the corresponding harmonic fuzzy partition is further defined. Then, based on the concept of symmetric harmonic partition, a new harmonic fuzzy local information C-means clustering (HLICM) is proposed and the local conver

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A., C. Okwor, Akpan A.G., and I. Agu C. "Enhancing Fuzzy C-Mean Algorithm for Medical Image Segmentation Process." Journal of Scientific and Engineering Research 9, no. 12 (2022): 127–37. https://doi.org/10.5281/zenodo.10532056.

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<strong>Abstract</strong> This paper is focus on an enhanced application of Fuzzy C-Means Algorithm in Image Segmentation Process. The application of digital systems in the areas of automated medical diagnosis is no doubt the backbone of any effective decision and treatment that may arise due to the cause of brain tumor. Several researchers have shown that the causes of most death are as a result of incorrect diagnosis of the affected areas of brain arising from brain tumor.  It is obvious that the chances of survival can be enhanced if the tumor is detected and classified correctly at it

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Rashwan, Shaheera, Mohamed Talaat Faheem, Amany Sarhan, and Bayumy A. B. Youssef. "A Wavelet Relational FuzzyC-Means Algorithm for 2D Gel Image Segmentation." Computational and Mathematical Methods in Medicine 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/430516.

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One of the most famous algorithms that appeared in the area of image segmentation is the FuzzyC-Means (FCM) algorithm. This algorithm has been used in many applications such as data analysis, pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Many modifications have been made to the algorithm to improve its segmentation quality. The proposed segmentation algorithm in this paper is based on the FuzzyC-Means algorithm adding the relational fuzzy notion and the wavelet transform to it so as to enhan

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Jeyaraman, Gowri, and Janakiraman Subbiah. "An Edge Exposure using Caliber Fuzzy C-means With Canny Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 8, no. 1 (2017): 59. http://dx.doi.org/10.11591/ijeecs.v8.i1.pp59-68.

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<p>Edge exposure or edge detection is an important and classical study of the medical field and computer vision. Caliber Fuzzy C-means (CFCM) clustering Algorithm for edge detection depends on the selection of initial cluster center value. This endeavor to put in order a collection of pixels into a cluster, such that a pixel within the cluster must be more comparable to every other pixel. Using CFCM techniques first cluster the BSDS image, next the clustered image is given as an input to the basic canny edge detection algorithm. The application of new parameters with fewer operations for

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Barkouk, Hamid, El Mokhtar En-Naimi, and Aziz Mahboub. "Performance evaluation of hierarchical clustering protocols with fuzzy C-means." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (2021): 3212. http://dx.doi.org/10.11591/ijece.v11i4.pp3212-3221.

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The longevity of the network and the lack of resources are the main problems within the WSN. Minimizing energy dissipation and optimizing the lifespan of the WSN network are real challenges in the design of WSN routing protocols. Load balanced clustering increases the reliability of the system and enhances coordination between different nodes within the network. WSN is one of the main technologies dedicated to the detection, sensing, and monitoring of physical phenomena of the environment. For illustration, detection, and measurement of vibration, pressure, temperature, and sound. The WSN can

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Hamid, Barkouk, Mokhtar En-Naimi EL, and Mahboub Aziz. "Performance evaluation of hierarchical clustering protocols with fuzzy C-means." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (2021): 3212–21. https://doi.org/10.11591/ijece.v11i4.pp3212-3221.

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The longevity of the network and the lack of resources are the main problems within the WSN. Minimizing energy dissipation and optimizing the lifespan of the WSN network are real challenges in the design of WSN routing protocols. Load balanced clustering increases the reliability of the system and enhances coordination between different nodes within the network. WSN is one of the main technologies dedicated to the detection, sensing, and monitoring of physical phenomena of the environment. For illustration, detection, and measurement of vibration, pressure, temperature, and sound. The WSN can

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Omidvar Tehrani, Iman, Subariah Ibrahim, and Habib Haron. "New Method to Optimize Initial Point Values of Spatial Fuzzy c-means Algorithm." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 5 (2015): 1035. http://dx.doi.org/10.11591/ijece.v5i5.pp1035-1044.

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Fuzzy based segmentation algorithms are known to be performing well on medical images. Spatial fuzzy C-means (SFCM) is broadly used for medical image segmentation but it suffers from optimum selection of seed point initialization which is done either manually or randomly. In this paper, an enhanced SFCM algorithm is proposed by optimizing the SFCM initial point values. In this method in order to increasing the algorithm speed first the approximate initial values are determined by calculating the histogram of the original image. Then by utilizing the GWO algorithm the optimum initial values cou

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Bhatti, Priha, Khalid Mahboob, Syed Saad Naeem, Iqra Heer Bhatti, and Noorulain Kamran. "Enhanced Diabetic Prediction Using Fuzzy C-Means Preprocessing and Random Forest Ensemble Learning." VFAST Transactions on Software Engineering 11, no. 4 (2023): 32–44. http://dx.doi.org/10.21015/vtse.v11i4.1657.

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Diabetes claims the lives of thousands each year, and many individuals remain oblivious to their condition until it reaches a critical stage. This study presents a data mining-based approach aimed at enhancing the early detection and prediction of diabetes, utilizing data from the Pima Indian Diabetes dataset. Despite the adaptability of fuzzy C-Means for various data types, the ultimate outcome of the clustering process hinges on the initial placement of cluster centers. Additionally, precision in data clustering is crucial; it can furnish either extensive, well-grouped data for the random fo

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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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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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Truong, Tung X., and Jong-Myon Kim. "An Enhanced Spatial Fuzzy C-Means Algorithm for Image Segmentation." Journal of the Korea Society of Computer and Information 17, no. 2 (2012): 49–57. http://dx.doi.org/10.9708/jksci.2012.17.2.049.

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Feng, Xue Bo, Fang Yao, Zhi Gang Li, and Xiao Jing Yang. "Improved Fuzzy C-Means Based on the Optimal Number of Clusters." Applied Mechanics and Materials 392 (September 2013): 803–7. http://dx.doi.org/10.4028/www.scientific.net/amm.392.803.

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According to the number of cluster centers, initial cluster centers, fuzzy factor, iterations and threshold, Fuzzy C-means clustering algorithm (FCM) clusters the data set. FCM will encounter the initialization problem of clustering prototype. Firstly, the article combines the maximum and minimum distance algorithm and K-means algorithm to determine the number of clusters and the initial cluster centers. Secondly, the article determines the optimal number of clusters with Silhouette indicators. Finally, the article improves the convergence rate of FCM by revising membership constantly. The imp

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Ganesh, M., M. Naresh, and C. Arvind. "MRI Brain Image Segmentation Using Enhanced Adaptive Fuzzy K-Means Algorithm." Intelligent Automation & Soft Computing 23, no. 2 (2017): 325–30. http://dx.doi.org/10.1080/10798587.2016.1231472.

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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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Chen, Ke, and Wen De Ke. "The Study of Invasion Examination Algorithm Based on Improvement Fuzzy C Means." Advanced Materials Research 267 (June 2011): 720–25. http://dx.doi.org/10.4028/www.scientific.net/amr.267.720.

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This paper put forward intrusion detection algorithm based on improved fuzzy C means (FCM) algorithm and execute the anomaly detection on KDDCUP data set, build intrusion detection system based improved algorithm and analyze the feasibility of the system. Through the fuzzy C means value's improvement algorithm, solve the fuzzy C means value algorithm problem that the algorithm sensitive to selection of the initial values and easily to fall in the local best solution. Thereby under the condition guarantee integrality and consistency of data attribute values, get rid of blindness of selecting in

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Du, Xinzhi. "A Robust and High-Dimensional Clustering Algorithm Based on Feature Weight and Entropy." Entropy 25, no. 3 (2023): 510. http://dx.doi.org/10.3390/e25030510.

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Since the Fuzzy C-Means algorithm is incapable of considering the influence of different features and exponential constraints on high-dimensional and complex data, a fuzzy clustering algorithm based on non-Euclidean distance combining feature weights and entropy weights is proposed. The proposed algorithm is based on the Fuzzy C-Means soft clustering algorithm to deal with high-dimensional and complex data. The objective function of the new algorithm is modified with the help of two different entropy terms and a non-Euclidean way of computing the distance. The distance calculation formula enha

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Haque, Mohammad A., and Jong-Myon Kim. "An enhanced fuzzy c-means algorithm for audio segmentation and classification." Multimedia Tools and Applications 63, no. 2 (2011): 485–500. http://dx.doi.org/10.1007/s11042-011-0921-z.

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Shi, Jiao, Yu Lei, Ying Zhou, and Maoguo Gong. "Enhanced rough–fuzzy c -means algorithm with strict rough sets properties." Applied Soft Computing 46 (September 2016): 827–50. http://dx.doi.org/10.1016/j.asoc.2015.12.031.

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Yang, Ying, Haoyu Chen, and Haoshen Wu. "A generalized fuzzy clustering framework for incomplete data by integrating feature weighted and kernel learning." PeerJ Computer Science 9 (October 5, 2023): e1600. http://dx.doi.org/10.7717/peerj-cs.1600.

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Missing data presents a challenge to clustering algorithms, as traditional methods tend to pad incomplete data first before clustering. To combine the two processes of padding and clustering and improve the clustering accuracy, a generalized fuzzy clustering framework is proposed based on optimal completion strategy (OCS) and nearest prototype strategy (NPS) with four improved algorithms developed. Feature weights are introduced to reduce outliers’ influence on the cluster centers, and kernel functions are used to solve the linear indistinguishability problem. The proposed algorithms are evalu

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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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Hamdani, Mostefa, and Youcef Aklouf. "Enhanced active VM load balancing algorithm using fuzzy logic and K-means clustering." Multiagent and Grid Systems 17, no. 1 (2021): 59–82. http://dx.doi.org/10.3233/mgs-210343.

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With the rapid development of data and IT technology, cloud computing is gaining more and more attention, and many users are attracted to this paradigm because of the reduction in cost and the dynamic allocation of resources. Load balancing is one of the main challenges in cloud computing system. It redistributes workloads across computing nodes within cloud to minimize computation time, and to improve the use of resources. This paper proposes an enhanced ‘Active VM load balancing algorithm’ based on fuzzy logic and k-means clustering to reduce the data center transfer cost, the total virtual

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Krasnov, Daniel, Dresya Davis, Keiran Malott, Yiting Chen, Xiaoping Shi, and Augustine Wong. "Fuzzy C-Means Clustering: A Review of Applications in Breast Cancer Detection." Entropy 25, no. 7 (2023): 1021. http://dx.doi.org/10.3390/e25071021.

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This paper reviews the potential use of fuzzy c-means clustering (FCM) and explores modifications to the distance function and centroid initialization methods to enhance image segmentation. The application of interest in the paper is the segmentation of breast tumours in mammograms. Breast cancer is the second leading cause of cancer deaths in Canadian women. Early detection reduces treatment costs and offers a favourable prognosis for patients. Classical methods, like mammograms, rely on radiologists to detect cancerous tumours, which introduces the potential for human error in cancer detecti

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Zaid, Afiqah Sofiya, Nor Hanimah Kamis, Zahari Md Rodzi, Adem Kilicman, and Norhidayah A Kadir. "An Improved Similarity-based Fuzzy Group Decision Making Model through Preference Transformation and K-Means Clustering Algorithm." Malaysian Journal of Fundamental and Applied Sciences 19, no. 6 (2023): 947–55. http://dx.doi.org/10.11113/mjfas.v19n6.3100.

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Group decision making plays a crucial role in organizational and community contexts, facilitating the exchange of expert opinions to arrive at effective decisions. The concept of preference, reflecting an individual's subjective evaluation of criteria or alternatives, forms a foundational element in this process. This study focuses on transforming non-fuzzy preferences, such as preference ordering and utility functions, into fuzzy preference relations (FPR) to address the uncertainty and uniformity inherent in expert preferences. To further enhance decision-making, we assess and visualize the

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