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Готові списки джерел за темами / Fuzyy C-Means / Статті в журналах

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Статті в журналах з теми "Fuzyy C-Means"

Автор: Grafiati

Опубліковано: 7 червня 2025

Оновлено: 2 серпня 2025

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1

Kara, Mehmet Akif, and Dilayla Bayyurt. "AB Üyesi ve AB Üyeliğine Aday Ülkelerin Sağlık Göstergelerine Göre Bulanık C-Ortalamalar ve Bulanık C-Medoids Yöntemleri ile Kümelenmesi." Turkish Journal of Statistics and Data Science 01, no. 1 (2025): 54–62. https://doi.org/10.5281/zenodo.15364774.

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2

Siti, Lailiyah, Yulsilviana Ekawati, and Andrea Reza. "Clustering analysis of learning style on anggana high school student." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 3 (2019): 1409–16. https://doi.org/10.12928/TELKOMNIKA.v17i3.9101.

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Анотація:

The inability of students to absorb the knowledge conveyed by the teacher is’nt caused by the inability of understanding and by the teacher which isn’t able to teach too, but because of the mismatch of learning styles between students and teachers, so that students feel uncomfortable in learning to a particular teacher. It also happens in senior high school (SHS/SMAN) 1 Anggana, so it is necessary to do this research, to analyze cluster (group) of student learning style by applying data mining method that is k-Means and Fuzzy C-Means. The purpose was to know the effectiveness of th

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3

Ko, Jeong-Won, Byung-In Choi, and Frank Chung-Hoon Rhee. "A Density Estimation based Fuzzy C-means Algorithm for Image Segmentation." Journal of Korean Institute of Intelligent Systems 17, no. 2 (2007): 196–201. http://dx.doi.org/10.5391/jkiis.2007.17.2.196.

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4

Lee, Kwang-Kyu, and Jung-Hyun Woo. "A Study of Fuzzy C-Means Clustering Noise Processing Method." Journal of Korean Institute of Communications and Information Sciences 44, no. 1 (2019): 124–29. http://dx.doi.org/10.7840/kics.2019.44.1.124.

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5

Zuherman, Rustam, Purwanto Aldi, Hartini Sri, and Stephani Saragih Glori. "Lung cancer classification using fuzzy c-means and fuzzy kernel C-Means based on CT scan image." International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (2021): 291–97. https://doi.org/10.11591/ijai.v10.i2.pp291-297.

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Cancer is one of the diseases with the highest mortality rate in the world. Cancer is a disease when abnormal cells grow out of control that can attack the body's organs side by side or spread to other organs. Lung cancer is a condition when malignant cells form in the lungs. To diagnose lung cancer can be done by taking x-ray images, CT scans, and lung tissue biopsy. In this modern era, technology is expected to help research in the field of health. Therefore, in this study feature extraction from CT images was used as data to classify lung cancer. We used CT scan image data from SPIE-AAP

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6

OHTA, Tomohiro, Muneki NEMOTO, Hidetomo ICHIHASHI, and Tetsuya MIYOSHI. "Hard Clustering by Fuzzy c-Means." Journal of Japan Society for Fuzzy Theory and Systems 10, no. 3 (1998): 532–40. http://dx.doi.org/10.3156/jfuzzy.10.3_532.

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7

Lee, Jung-Ho, Won-Woo Lee, Joong-Hoon Kim, and Hwan-Don Jun. "Estimation of Urban Inundation Risk using Fuzzy C-Means." Journal of Korean Society of Hazard Mitigation 11, no. 4 (2011): 229–35. http://dx.doi.org/10.9798/kosham.2011.11.4.229.

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8

Falidazia, Hasanah Faizana, Kartikasari Puspita, and Fakhriyana Deby. "Implementation of Fuzzy Possibilistic C-Means with Optimal Membership of Fuzzy C-Means for Stunting Management in Central Java Province." International Journal of Mathematics and Computer Research 13, no. 05 (2025): 5227–33. https://doi.org/10.5281/zenodo.15524233.

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Stunting is a serious problem makes children vulnerable disease and reduced productivity. According to Indonesian Health Survey (2023), stunting rate Indonesia in 2023 was 21.5%. The target set in 2020-2024 National Medium-Term Development Plan of 14% and WHO standard below 20% have still not been achieved. Based on Indonesian Health Survey (2023), prevalence stunting in Central Java Province in 2023 has decreased only 0.1% to 20.7%. Therefore, it is necessary to evaluate acceleration stunting handling from achievement more focused and targeted Special Index for Stunting Management (IKPS) indi

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9

Stetco, Adrian, Xiao-Jun Zeng, and John Keane. "Fuzzy C-means++: Fuzzy C-means with effective seeding initialization." Expert Systems with Applications 42, no. 21 (2015): 7541–48. http://dx.doi.org/10.1016/j.eswa.2015.05.014.

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10

Kusnadi, Adhi, and Abi Kabisah Maulillah. "Perbandingan Algoritma C-Means Clustering dan Fuzzy C-Means Clustering." Ultima Computing : Jurnal Sistem Komputer 11, no. 1 (2019): 51–54. http://dx.doi.org/10.31937/sk.v11i1.953.

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Анотація:

Salah satu operasi di dalam analisis citra adalah segmentasi citra. Pada mulanya proses segmentasi dilakukan untuk memisahkan objek dari latar belakangnya, sehingga segmentasi merupakan bagian penting dalam pengenalan objek. Saat ini segmentasi sudah mengalami perkembangan yang sangat pesat, bukan hanya untuk tujuan pengenalan objek saja tetapi juga untuk persoalan interpretasi citra, yaitu untuk mengetahui objek-objek yang termuat dalam suatu citra. Banyak algoritma sudah dikembangkan untuk proses segmentasi citra. Beberapa di antaranya adalah algoritma C-Means Clustering dan Fuzzy C-Means Cl

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11

Heo, Gyeong-Yong, Young-Hwan NamKoong, and Seong-Hoon Kim. "An Extension of Possibilistic Fuzzy C-means using Regularization." Journal of the Korea Society of Computer and Information 15, no. 1 (2010): 43–50. http://dx.doi.org/10.9708/jksci.2010.15.1.043.

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12

Heo, Gyeong-Yong, Jin-Seok Seo, and Im-Geun Lee. "Problems in Fuzzy c-means and Its Possible Solutions." Journal of the Korea Society of Computer and Information 16, no. 1 (2011): 39–46. http://dx.doi.org/10.9708/jksci.2011.16.1.039.

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13

ANANTH, CHRISTO. "Enhancing Segmentation Approaches from Oaam to Fuzzy K-C-Means." Journal of Research on the Lepidoptera 51, no. 2 (2020): 1086–108. http://dx.doi.org/10.36872/lepi/v51i2/301159.

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14

Wutsqa, Dhoriva Urwatul. "Fuzzy C-means Clustering for Landslide Mapping in Malang Regency." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 1653–59. http://dx.doi.org/10.5373/jardcs/v12sp7/20202271.

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15

Jayasree, R., and N. A. Sheela Selvakumari. "Analyzing Student Performance using Fuzzy Possibilistic C-Means Clustering Algorithm." Indian Journal Of Science And Technology 16, no. 38 (2023): 3230–35. http://dx.doi.org/10.17485/ijst/v16i38.226.

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16

Rustam, Zuherman, Annisa Kamalia, Rahmat Hidayat, Fajar Subroto, and Aditya Suryansyah S. "Comparison of Fuzzy C-Means, Fuzzy Kernel C-Means, and Fuzzy Kernel Robust C-Means to Classify Thalassemia Data." International Journal on Advanced Science, Engineering and Information Technology 9, no. 4 (2019): 1205. http://dx.doi.org/10.18517/ijaseit.9.4.9580.

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17

Gubu, La, Edi Cahyono, Arman Arman, Herdi Budiman, and Muh Kabil Djafar. "OPTIMASI PORTOFOLIO MEAN-VARIANCE DENGAN ANALISIS KLASTER FUZZY C-MEANS." Jurnal Gaussian 12, no. 4 (2024): 593–604. http://dx.doi.org/10.14710/j.gauss.12.4.593-604.

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18

Heo, Gyeong-Yong, and Kwang-Baek Kim. "Initialization of Fuzzy C-Means Using Kernel Density Estimation." Journal of the Korean Institute of Information and Communication Engineering 15, no. 8 (2011): 1659–64. http://dx.doi.org/10.6109/jkiice.2011.15.8.1659.

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19

Hadi, Mahdipour, Khademi Morteza, and Sadoghi Yazdi Hadi. "Vector fuzzy C-means." Journal of Intelligent & Fuzzy Systems 24, no. 2 (2013): 363–81. http://dx.doi.org/10.3233/ifs-2012-0561.

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20

Pedrycz, Witold. "Conditional Fuzzy C-Means." Pattern Recognition Letters 17, no. 6 (1996): 625–31. http://dx.doi.org/10.1016/0167-8655(96)00027-x.

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21

Han, Se-Jin, Jae-Beom Myoung, Jae Sakong, Sung-Choong Woo, and Tae-Won Kim. "Analysis of Wound Evidence and Prediction of Threat Shape Based on the Fuzzy C-Means Clustering." Transactions of the Korean Society of Mechanical Engineers - A 43, no. 12 (2019): 891–901. http://dx.doi.org/10.3795/ksme-a.2019.43.12.891.

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22

Hadi, Rosalia, I. Ketut Gede Darma Putra, and I. Nyoman Satya Kumara. "Penentuan Kompetensi Mahasiswa dengan Algoritma Genetik dan Metode Fuzzy C-Means." Majalah Ilmiah Teknologi Elektro 15, no. 2 (2016): 101–6. http://dx.doi.org/10.24843/mite.1502.15.

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23

Roh, Seok-Beom, Tae-Chon Ahn, Yong-Sun Baek, and Yong-Soo Kim. "Space Partition using Context Fuzzy c-Means Algorithm for Image Segmentation." Journal of Korean Institute of Intelligent Systems 20, no. 3 (2010): 368–74. http://dx.doi.org/10.5391/jkiis.2010.20.3.368.

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24

Aqil, Muhammad, Ichiro Kita, Akira Yano, and Soichi Nishiyama. "Groundwater Pump Clustering Management Strategy Using a Fuzzy C-Means Approach." Journal of Rainwater Catchment Systems 12, no. 2 (2007): 17–22. http://dx.doi.org/10.7132/jrcsa.kj00004557610.

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25

Wang Chao, 王超, 王永顺 Wang Yongshun та 狄凡 Di Fan. "快速自动模糊C-均值聚类彩色图像分割算法". Laser & Optoelectronics Progress 59, № 22 (2022): 2210001. http://dx.doi.org/10.3788/lop202259.2210001.

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26

Dewangan Asha Ambhaikar, Omprakash. "Extended Fuzzy C-Means Clustering Algorithm in Segmentation of Noisy Images." International Journal of Science and Research (IJSR) 1, no. 2 (2012): 16–19. http://dx.doi.org/10.21275/ijsr11120204.

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27

Tang, Ai Hong, Lian Cai, and You Mei Zhang. "Application of Hard C-Means and Fuzzy C-Means in Data Fusion." Applied Mechanics and Materials 190-191 (July 2012): 265–68. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.265.

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Анотація:

This article describes two kinds of Fuzzy clustering algorithm based on partition,Fuzzy C-means algorithm is on the basis of the hard C-means algorithm, and get a big improvement, making large data similarity as far as possible together. As a result of Simulation, FCM algorithm has more reasonable than HCM method on convergence, data fusion, and so on.

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28

Apsari, Gadis Retno, Mohammad Syaiful Pradana, and Novita Eka Chandra. "Implementasi Fuzzy C-Means dan Possibilistik C-Means Pada Data Performance Mahasiswa." Unisda Journal of Mathematics and Computer Science (UJMC) 6, no. 2 (2020): 39–48. http://dx.doi.org/10.52166/ujmc.v6i2.2392.

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Students are the most important component in a university, especially private universities especially Universitas Islam Darul ‘ulum (Unisda) Lamongan. One of the most important roles of students for higher education is achievement. This study aims to determine the role of Fuzzy Clustering in classifying student performance data. The data includes GPA (Grade Point Average), ECCU (Extra-Curricular Credit Unit), attendance, and students' willingness to learn. So that groups of students who have the potential to have achievements can be identified. In this case, the grouping of student performance

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29

Zhou, Huiyu, Gerald Schaefer, Abdul H. Sadka, and M. Emre Celebi. "Anisotropic Mean Shift Based Fuzzy C-Means Segmentation of Dermoscopy Images." IEEE Journal of Selected Topics in Signal Processing 3, no. 1 (2009): 26–34. http://dx.doi.org/10.1109/jstsp.2008.2010631.

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30

G.Ravindran1, T.Joby Titus2* V.Ganesh3 V.S.Sanjana Devi4. "ANALYSIS OF IMAGE SEGMENTATION TECHNIQUES FOR TEXTURE FEATURE EXTRACTION." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 6 (2017): 66–71. https://doi.org/10.5281/zenodo.802824.

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Анотація:

The pixels of an image are grouped into several regions for segmentation. In segmentation technique the texture feature parameter is an image analysis technique in the field of Computer vision. In the Segmentation field, there are many techniques are used to segment the images .The proposed approach is to analyze and compare the gray level texture feature techniques, number of clusters, Fuzzy C means, and to find which algorithmic approach provides better results in image segmentation.

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31

Wan, Shuting, and Xiong Zhang. "Bearing fault diagnosis based on teager energy entropy and mean-shift fuzzy C-means." Structural Health Monitoring 19, no. 6 (2020): 1976–88. http://dx.doi.org/10.1177/1475921720910710.

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Feature extraction and fault recognition of vibration signals are two important parts of bearing fault diagnosis. In this article, a fault diagnosis method based on teager energy entropy of each wavelet subband and improved fuzzy C-means is proposed. First, bearing vibration signal is decomposed into wavelet packet and normalized teager energy entropy feature matrix is constructed as clustering index. Principal component analysis is applied to the high-dimensional teager energy entropy feature matrix, and the principal components are determined by cumulative contribution rate to construct feat

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32

R, Jayasree, and A. Sheela Selvakumari N. "Analyzing Student Performance using Fuzzy Possibilistic C-Means Clustering Algorithm." Indian Journal of Science and Technology 16, no. 38 (2023): 3230–35. https://doi.org/10.17485/IJST/v16i38.226.

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Abstract <strong>Objectives:</strong> This work is to propose a more effective Fuzzy C-means clustering algorithm for predicting student performance based on their health. <strong>Methods:</strong> The standard dataset is collected from UCI repository. This study proposes FPCM-SPP clustering algorithm which is compared with traditional algorithms like K-Means, K-Medoids, and Fuzzy C-Means using student data from secondary education at two Portuguese institutions (2008). Based on the clustering accuracy, mean squared error, and cluster formation time, the performance of the clust

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33

Sathishkumar, Dr Balamurugan, Dr Akpojaro Jackson, and Ramalingam Dr. M. "Gene Expression Using Artificial Bee Colony besides Fuzzy C Means and NFDA." International Journal of Psychosocial Rehabilitation 24, no. 03 (2020): 2028–36. http://dx.doi.org/10.37200/ijpr/v24i3/pr200949.

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34

Joo, Won-Hee, and Frank Chung-Hoon Rhee. "A Novel Approach towards use of Adaptive Multiple Kernels in Interval Type-2 Possibilistic Fuzzy C-Means." Journal of Korean Institute of Intelligent Systems 24, no. 5 (2014): 529–35. http://dx.doi.org/10.5391/jkiis.2014.24.5.529.

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35

Jebari, Khalid, Abdelaziz Elmoujahid, and Aziz Ettouhami. "Automatic Genetic Fuzzy c-Means." Journal of Intelligent Systems 29, no. 1 (2018): 529–39. http://dx.doi.org/10.1515/jisys-2018-0063.

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Abstract Fuzzy c-means is an efficient algorithm that is amply used for data clustering. Nonetheless, when using this algorithm, the designer faces two crucial choices: choosing the optimal number of clusters and initializing the cluster centers. The two choices have a direct impact on the clustering outcome. This paper presents an improved algorithm called automatic genetic fuzzy c-means that evolves the number of clusters and provides the initial centroids. The proposed algorithm uses a genetic algorithm with a new crossover operator, a new mutation operator, and modified tournament selectio

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36

Demirhan, Haydar. "Mixed fuzzy C-means clustering." Information Sciences 690 (February 2025): 121528. http://dx.doi.org/10.1016/j.ins.2024.121528.

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37

Dagher, Issam. "Complex fuzzy c-means algorithm." Artificial Intelligence Review 38, no. 1 (2011): 25–39. http://dx.doi.org/10.1007/s10462-011-9239-5.

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38

Leski, Jacek M. "Fuzzy c -ordered-means clustering." Fuzzy Sets and Systems 286 (March 2016): 114–33. http://dx.doi.org/10.1016/j.fss.2014.12.007.

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39

Bezdek, James. "Fuzzy C-means cluster analysis." Scholarpedia 6, no. 7 (2011): 2057. http://dx.doi.org/10.4249/scholarpedia.2057.

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40

Karayiannis, Nicolaos B. "Generalized fuzzy c-means algorithms." Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology 8, no. 1 (2000): 63–81. https://doi.org/10.3233/ifs-2000-098.

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41

Safitri, Qonita Ummi, Arief Fatchul Huda, and Asep Solih Awaludin. "SEGMENTASI CITRA MENGGUNAKAN ALGORITMA FUZZY c-MEANS (FCM) DAN SPATIAL FUZZY c-MEANS (sFCM)." Kubik: Jurnal Publikasi Ilmiah Matematika 2, no. 1 (2017): 22–34. http://dx.doi.org/10.15575/kubik.v2i1.1471.

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Анотація:

Pengolahan citra merupakan salah satu aplikasi yang dimanfaatkan dalam kehidupan. Salah satu kajian pengolahan citra adalah segmentasi. Segmentasi citra dilakukan dengan banyak pendekatan, diantaranya pedekatan klastering. Algoritma klastering yang digunakan pada segmentasi citra, umumnya berbasis fuzzy c-means. Fuzzy c-mean (FCM) membagi citra menjadi beberapa wilayah tingkat keabuan berdasarkan derajat keanggotaan pada rentang [0,1]. FCM kurang memanfaatkan informasi spasial, yang merupakan atribut penting dalam proses segmentasi citra. Oleh karena itu, Chuang dkk (2006) menambahkan fungsi s

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42

A, Dharmarajan, and Velmurugan T. "Performance Analysis on K-Means and Fuzzy C-Means Clustering Algorithms Using CT-DICOM Images of Lung Cancer." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (2019): 494–502. http://dx.doi.org/10.5373/jardcs/v11/20192597.

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43

Banumathi, A., and A. Pethalakshmi. "Refinement of K-Means and Fuzzy C-Means." International Journal of Computer Applications 39, no. 17 (2012): 11–16. http://dx.doi.org/10.5120/4911-7441.

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44

Sihombing, Pardomuan Robinson, Yoshep Paulus Apri Caraka Yuda, Busminoloan Busminoloan, and Iis Hayyun Nurul Islam. "KOMPARASI PERFORMA K-MEANS DAN FUZZY C-MEANS." Jurnal Bayesian : Jurnal Ilmiah Statistika dan Ekonometrika 2, no. 2 (2022): 125–32. http://dx.doi.org/10.46306/bay.v2i2.35.

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Анотація:

This study aims to test the performance of the K-Means Cluster method with Fuzzy C-Means. The data used is data from the Inclusive Economic Development Index in 34 provinces in Indonesia in 2021. The data is sourced from Bappenas. The optimum number of clusters suggested using the Elbow method technique is as many as 4 clusters. By paying attention to the silouhette value the K-Means method is as good as the Fuzzi C-Means. However, the K-Means method is better than the Fuzzy C-Means model when viewed based on the criteria of smaller AIC and BIC values and a larger R 2. The provinces of Papua a

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45

Gharnali, Babak, and Siavash Alipour. "MRI Image Segmentation Using Conditional Spatial FCM Based on Kernel-Induced Distance Measure." Engineering, Technology & Applied Science Research 8, no. 3 (2018): 2985–90. https://doi.org/10.5281/zenodo.1400543.

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Fuzzy C-means (FCM) clustering is the widest spread clustering approach for medical image segmentation because of its robust characteristics for data classification. But, it does not fully utilize the spatial information and is therefore very sensitive to noise and intensity inhomogeneity in magnetic resonance imaging (MRI). In this paper, we propose a conditional spatial kernel fuzzy C-means (CSKFCM) clustering algorithm to overcome the mentioned problem. The approach consists of two successive stages. First stage is achieved through the incorporation of local spatial interaction among adjace

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46

Kim, Jong-Ho, Sang-Kyoon Kim, Goo-Seun Hang, Sang-Ho Ahn, and Byoung-Doo Kang. "An Object Detection and Tracking System using Fuzzy C-means and CONDENSATION." Journal of the Korea Industrial Information Systems Research 16, no. 4 (2011): 87–98. http://dx.doi.org/10.9723/jksiis.2011.16.4.087.

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Nelson, Butarbutar, Perdana Windarto Agus, Hartama Dedy, and Solikhun. "KOMPARASI KINERJA ALGORITMA FUZZY C-MEANS DAN K-MEANS DALAM PENGELOMPOKAN DATA SISWA BERDASARKAN PRESTASI NILAIAKADEMIK SISWA." Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) 1, no. 1 (2016): 46–55. https://doi.org/10.5281/zenodo.546761.

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Various efforts have been undertaken by schools to improve academic achievement students in an effort to achieve national education standards. One of which is by doing tutoring for each student, but the results have not been so satisfying. This is due to the particular school education section did not fully understand each student's ability to master an eye teaching core subjects, especially the UN. To overcome this by utilizing clustering techniques will do the data grouping students based on merit value academic sources of data obtained directly from the education department. With use cluste

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Schaefer, Gerald, Qinghua Hu, Huiyu Zhou, James F. Peters, and Aboul Ella Hassanien. "Rough C-means and Fuzzy Rough C-means for Colour Quantisation." Fundamenta Informaticae 119, no. 1 (2012): 113–20. http://dx.doi.org/10.3233/fi-2012-729.

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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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Rustam, Zuherman, Aldi Purwanto, Sri Hartini, and Glori Stephani Saragih. "Lung cancer classification using fuzzy c-means and fuzzy kernel C-Means based on CT scan image." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (2021): 291. http://dx.doi.org/10.11591/ijai.v10.i2.pp291-297.

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<span id="docs-internal-guid-94842888-7fff-2ae1-cd5c-026943b95b7f"><span>Cancer is one of the diseases with the highest mortality rate in the world. Cancer is a disease when abnormal cells grow out of control that can attack the body's organs side by side or spread to other organs. Lung cancer is a condition when malignant cells form in the lungs. To diagnose lung cancer can be done by taking x-ray images, CT scans, and lung tissue biopsy. In this modern era, technology is expected to help research in the field of health. Therefore, in this study feature extraction from CT images w

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