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Relevant bibliographies by topics / Enhanced k-means and fuzzy c-mean algorithms

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Academic literature 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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Journal articles on the topic "Enhanced k-means and fuzzy c-mean algorithms"

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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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Book chapters on the topic "Enhanced k-means and fuzzy c-mean algorithms"

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Tolentino, Joven A., Bobby D. Gerardo, and Ruji P. Medina. "Enhanced Manhattan-Based Clustering Using Fuzzy C-Means Algorithm." In Recent Advances in Information and Communication Technology 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93692-5_13.

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Sri Nagesh, A., G. P. Saradhi Varma, A. Govardhan, and B. Raveendra Babu. "An Enhanced Fuzzy C-Means Clustering (ECFMC) Algorithm for Spot Segmentation." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27183-0_34.

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Singh, Munendra, C. S. Asha, and Neeraj Sharma. "Multi-objective Particle Swarm Optimization Based Enhanced Fuzzy C-Means Algorithm for the Segmentation of MRI Data." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2761-3_90.

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Vigneshwaran, S., G. Vishnuvarthanan, M. Pallikonda Rajasekaran, and T. Arunprasath. "Extraction of Lesion and Tumor Region in Multi-modal Images Using Novel Self-organizing Map-Based Enhanced Fuzzy C-Means Clustering Algorithm." In Lecture Notes in Electrical Engineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1906-8_73.

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Gurumoorthy, Sasikumar, and B. K. Tripathy. "Intelligent Technique to Identify Epilepsy Using Fuzzy Firefly System for Brain Signal Processing." In Advances in Computational Intelligence and Robotics. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2857-9.ch020.

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In the new direction of understand the signal that is created from the brain organization is one of the main chores in the brain signal processing. Amid all the neurological disorders the human brain epilepsy is measured as one of the extreme prevalent and then programmed artificial intelligence detection technique is an essential due to the crooked and unpredictable nature of happening of epileptic seizures. We proposed an Improved Fuzzy firefly algorithm, which would enhance the classification of the brain signal efficiently with minimum iteration. An important bunching technique created on

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Prasant, P. "In-Depth Exploration of Unsupervised Learning Algorithms and Techniques for Pattern Discovery." In Artificial Intelligence and Machine Learning. RADemics Research Institute, 2024. https://doi.org/10.71443/9788197282164-07.

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Unsupervised learning has emerged as a powerful tool for uncovering hidden patterns and structures within complex datasets, making it indispensable across various domains. This book chapter provides an in-depth exploration of unsupervised learning algorithms, with a particular focus on advanced clustering techniques, including spectral clustering, mean-shift clustering, agglomerative hierarchical clustering, density-based clustering, and fuzzy C-means clustering. The chapter delves into the challenges and limitations of each algorithm, offering insights into their applicability and performance

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Liu, Yang, Kunyuan Hu, Yunlong Zhu, and Hanning Chen. "An enhanced Kernel Fuzzy C-Means Algorithm based on bio-inspired computing methods." In Electronics, Information Technology and Intellectualization. CRC Press, 2014. http://dx.doi.org/10.1201/b17988-28.

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Acharjya, Debi Prasanna, and Chiranji Lal Chowdhary. "Breast Cancer Detection Using Hybrid Computational Intelligence Techniques." In Research Anthology on Medical Informatics in Breast and Cervical Cancer. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7136-4.ch022.

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Diagnosis of cancer is of prime concern in recent years. Medical imaging is used to analyze these diseases. But, these images contain uncertainties due to various factors and thus intelligent techniques are essential to process these uncertainties. This chapter highlights two hybridizations pertaining to breast cancer. In one hybridization technique, it hybridizes intuitionistic fuzzy set and rough set in combination with statistical feature extraction methods. In the second case, intuitionistic fuzzy histogram hyperbolization is hybridized with possibilistic fuzzy c-mean clustering algorithm.

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Acharjya, Debi Prasanna, and Chiranji Lal Chowdhary. "Breast Cancer Detection Using Hybrid Computational Intelligence Techniques." In Advances in Healthcare Information Systems and Administration. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5460-8.ch011.

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Diagnosis of cancer is of prime concern in recent years. Medical imaging is used to analyze these diseases. But, these images contain uncertainties due to various factors and thus intelligent techniques are essential to process these uncertainties. This chapter highlights two hybridizations pertaining to breast cancer. In one hybridization technique, it hybridizes intuitionistic fuzzy set and rough set in combination with statistical feature extraction methods. In the second case, intuitionistic fuzzy histogram hyperbolization is hybridized with possibilistic fuzzy c-mean clustering algorithm.

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Elahi, Bukhtawar, Maria Kanwal, and Sana Elahi. "Analysis of Different Image Processing Techniques for Classification and Detection of Cancer Cells." In Advances in Medical Technologies and Clinical Practice. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2521-0.ch008.

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This chapter gives an analysis of various methodologies for detecting cancer cells through image processing techniques. The challenges during such detections are over-segmentation and computational complexities. Therefore, the algorithms dealing with such problems are analyzed in this chapter. In these algorithms, a watershed and setting up threshold are helpful to overcome segmentation issues. A support vector machine is discussed to detect subtypes of pneumoconiosis for disjointing segments of lungs. For finding lung cancer cells, a segmentation weighted fuzzy probabilistic-based clustering

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Conference papers on the topic "Enhanced k-means and fuzzy c-mean algorithms"

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Abreu, Pedro F. F., Luís H. de O. Mendes, Geraldo A. Sarmento Neto, et al. "LoRaWISEP+: A Comprehensive Tool for Strategic Gateway Placement in LoRaWAN Networks." In Anais Estendidos do Simpósio Brasileiro de Engenharia de Sistemas Computacionais. Sociedade Brasileira de Computação - SBC, 2024. https://doi.org/10.5753/sbesc_estendido.2024.243700.

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The Internet of Things has revolutionized device interconnectivity, with Low Power Wide Area Networks (LPWAN) enabling long-range communication with minimal energy consumption. Within this context, LoRaWAN is widely recognized as an efficient and robust technology for LPWAN networks. However, strategic gateway deployment remains a challenging for optimizing network performance. In this way, this paper issue proposes LoRaWISEP+, an enhanced version of the LoRaWISEP system, consisting of a comprehensive tool designed to improve the deployment process of LoRaWAN gateways. This solution incorporat

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Tehrani, Iman Omidvar, and Subariah Ibrahim. "An enhanced fuzzy c-means medical segmentation algorithm." In 2014 International Symposium on Biometrics and Security Technologies (ISBAST). IEEE, 2014. http://dx.doi.org/10.1109/isbast.2014.7013136.

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Bharill, Neha, and Aruna Tiwari. "Enhanced cluster validity index for the evaluation of optimal number of clusters for Fuzzy C-Means algorithm." In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891591.

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Gurugubelli, Venkata Sukumar, Zhouzhou Li, Honggang Wang, and Hua Fang. "eFCM: An Enhanced Fuzzy C-Means Algorithm for Longitudinal Intervention Data." In 2018 International Conference on Computing, Networking and Communications (ICNC). IEEE, 2018. http://dx.doi.org/10.1109/iccnc.2018.8390419.

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Nsaif, Aamr Subhi, and Fadhil Hanoon Abbood. "A parallel extended fuzzy c-mean algorithm for enhanced color image segmentation." In INTERNATIONAL WORKSHOP ON MACHINE LEARNING AND QUANTUM COMPUTING APPLICATIONS IN MEDICINE AND PHYSICS: WMLQ2022. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0203632.

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hussein shakah, Ghazi, and Mohammad Anwar Assaad. "Optimizing Health Pattern Recognition: A Fuzzy C-Means and Particle Swarm Optimization Approach for Enhanced Neural Network Performance." In 5TH INTERNATIONAL CONFERENCE ON COMMUNICATION ENGINEERING AND COMPUTER SCIENCE (CIC-COCOS'24). Cihan University-Erbil, 2024. http://dx.doi.org/10.24086/cocos2024/paper.1510.

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Health pattern recognition is vital for advancing personalized healthcare interventions. This research introduces a synergistic approach, combining Fuzzy C-Means clustering with Particle Swarm Optimization (PSO), to optimize the hyperparameters of an Artificial Neural Network (ANN) and enhance health pattern recognition. Leveraging key features such as 'Smoker,' 'BMI,' and 'GenHlth,' Fuzzy C-Means reveals distinctive health clusters, providing nuanced insights into diverse health profiles within the dataset. Subsequently, the PSO algorithm systematically optimizes critical ANN hyperparameters,

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Halder, Amiya, Rudrajit Choudhuri, and Apurba Sarkar. "Enhanced Kernelized Conditional Spatial Fuzzy C Means Algorithm for Noisy Brain MRI Tissue Segmentation." In ICVGIP'22: Thirteenth Indian Conference on Computer Vision, Graphics and Image Processing. ACM, 2022. http://dx.doi.org/10.1145/3571600.3571644.

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Ammisetty, Veeraswamy, Vs Sudhakar Rao Ande, D. Kishore Babu, and Mikkili Baburao. "Integrating Decision Theory and Syntactic Data for Enhanced Rough Fuzzy C-Means Clustering Algorithm." In 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS). IEEE, 2023. http://dx.doi.org/10.1109/icacrs58579.2023.10404288.

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Vivona, L., D. Cascio, R. Magro, F. Fauci, and G. Raso. "A fuzzy logic C-means clustering algorithm to enhance microcalcifications clusters in digital mammograms." In 2011 IEEE Nuclear Science Symposium and Medical Imaging Conference (2011 NSS/MIC). IEEE, 2011. http://dx.doi.org/10.1109/nssmic.2011.6152551.

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Amsini, P., and R. Uma Rani. "Enhanced Type 2 Triangular Intuitionistic Fuzzy C Means Clustering Algorithm for Breast Cancer Histopathology Images." In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2020. http://dx.doi.org/10.1109/iccmc48092.2020.iccmc-000110.

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