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Journal articles on the topic 'Fuzzy K-means'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

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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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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Hutin, Adam Al Avin Faisal. "Clustering Job Seekers in Bojonegoro Using K-Means and Fuzzy K-Means." Jurnal Statistika dan Komputasi 4, no. 1 (2025): 33–46. https://doi.org/10.32665/statkom.v4i1.4651.

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Background: Job seekers are part of the labor force who are unemployed and actively looking for work. One of the efforts to address the rising number of job seekers is by expanding job openings or employment opportunities. Employment is an essential need for individuals to meet various aspects of life, ranging from basic needs to education and housing. Objective: This paper aims to analyze the frequency distribution of job seeker attributes in Bojonegoro, compare the K-Means and Fuzzy K-Means methods in clustering sub-districts, determine the best clustering method, and describe frequency dist

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Lee, Soo-Hyun, Jae-Yun Kim, and Young-Seon Jeong. "Various Validity Indices for Fuzzy K-means Clustering." korean management review 46, no. 4 (2017): 1201–26. http://dx.doi.org/10.17287/kmr.2017.46.4.1201.

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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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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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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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Rahmah, Lathifatur. "PERBANDINGAN HASIL PENGGEROMBOLAN K-MEANS, FUZZY K-MEANS, DAN TWO STEP CLUSTERING." Jurnal Pendidikan Matematika 2, no. 1 (2017): 39. http://dx.doi.org/10.18592/jpm.v2i1.1166.

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Analisis gerombol merupakan salah satu metode peubah ganda yang tujuan utamanya adalah mengelompokkan objek berdasarkan kemiripan atau ketidakmiripan karakteristik-karakteristiknya, sehingga objek yang terletak dalam satu gerombol memiliki kemiripan sifat yang lebih besar dibandingkan dengan objek pengamatan yang terletak pada gerombol lain. K-means merupakan salah satu metode penggerombolan tak berhirarki yang paling banyak digunakan, namun karena menggunakan rataan sebagai centroidnya, metode ini lebih sensitif terhadap keberadaan pencilan pada data. Sehingga berkembanglah metode baru, k-med

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Hot, Elma, and Vesna Popovic-Bugarin. "Soil data clustering by using K-means and fuzzy K-means algorithm." Telfor Journal 8, no. 1 (2016): 56–61. http://dx.doi.org/10.5937/telfor1601056h.

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Nuraeni, Fitri, Helfy Susilawati, and Yoga Handoko Agustin. "PERBANDINGAN IMPLEMENTASI ALGORITMA K-MEANS++ DAN FUZZY C-MEANS PADA SEGMENTASI CITRA WAJAH." JuTI "Jurnal Teknologi Informasi" 1, no. 2 (2023): 47. http://dx.doi.org/10.26798/juti.v1i2.722.

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Dalam pengenalan wajah menggunakan metode pengolahan citra, dibutuhkan proses segmentasi citra agar dapat dilakukan proses analisis citra selanjutnya. Segmentasi citra dapat dilakukan dengan metode clustering yang memiliki beberapa algoritma berbasis centroid, seperti k-means dan fuzzy c-means. Algoritma k-means sendiri memiliki beberapa varian, salah satunya k-means++ dimana varian ini lebih cerdas dalam memilih inisial centroid dibanding k-means yang memilih inisial centroid secara acak. Algoritma fuzzy cmeans sendiri telah memiliki keunggulan dalam engelompokan objek yang tersebar secara ti

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Shinde, Ganeshchandra Narharrao, Inamdar S. A., and Narangale S.M. "Fuzzy Mean Point Clustering using K-means algorithm for implementing the movecentroid function code." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 1 (2013): 54–56. http://dx.doi.org/10.24297/ijct.v4i1b.3059.

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The paper focus on combination of K-Means algorithm for Fuzzy Mean Point Clustering Neural Network (FMPCNN). The algorithm is implemented in JAVA program code for implementing the movecentroid function code into FMPCNN.Â Here we have provided movecentroidâ€™s output to Fuzzy clustering as criteria, movecentroid is the base function of K-means algorithm as in Fuzzy Mean Point Clustering Neural Network (FMPCNN) algorithm, calculation of cluster based on pre-defined criteria and scope is done.Â In the experiment we have used four datasets and observed results in nano seconds there is huge differe

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Gubu, La, Edi Cahyono, Arman Arman, Herdi Budiman, and Muh Kabil Djafar. "Family of K-Means Clustering for Robust Mean-Variance Portfolio Selection: A Comparison of K-Medoids, K-Means, and Fuzzy C-Means." Industrial Engineering & Management Systems 23, no. 3 (2024): 342–56. http://dx.doi.org/10.7232/iems.2024.23.3.342.

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Al Rivan, Muhammad Ezar, Steven Steven, and William Tanzil. "Optimasi Fuzzy C-Means dan K-Means Menggunakan Algoritma Genetika untuk Pengklasteran Dataset Diabetic Retinopathy." Jurnal Teknologi Informasi dan Ilmu Komputer 7, no. 5 (2020): 993. http://dx.doi.org/10.25126/jtiik.2020711872.

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<p class="Abstrak"><em>Diabetic Retinopathy</em> adalah komplikasi dari diabetes yang mengakibatkan gangguan pada retina mata. Gangguan tersebut dapat diketahui dengan deteksi awal melalui data yang diekstraksi dari citra mata. Deteksi awal dapat dilakukan dengan menggunakan metode <em>clustering</em>. Metode yang digunakan yaitu <em>Fuzzy C-Means</em> dan <em>K-Means</em>. <em>Fuzzy C-Means</em> dan <em>K-Means</em> memiliki kelemahan dari jumlah iterasi yang besar. Jumlah iterasi pada <em>Fuzzy C-Means</em

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Febrianti, Fitria, Moh Hafiyusholeh, and Ahmad Hanif Asyhar. "PERBANDINGAN PENGKLUSTERAN DATA IRIS MENGGUNAKAN METODE K-MEANS DAN FUZZY C-MEANS." Jurnal Matematika "MANTIK" 2, no. 1 (2016): 7. http://dx.doi.org/10.15642/mantik.2016.2.1.7-13.

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Indonesia with abundant natural resources, certainly have a lot of plants are innumerable. To clasify the plants into different clusters can use several methods. Methods used are K-Means and Fuzzy C-Means. However, this methods have difference. Not only in terms of algorithms, but in terms of value calculation on the root mean square error (RMSE) also different. To calculate the value of RMSE there are two indicators are required, namelt the training data and the checking data. Of discussion, the Fuzzy C-Means method has RMSE values smaller than the K-Means method, namely on 80 training data a

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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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Syukron, Hamdi, Muhammad Fauzi Fayyad, Farin Junita Fauzan, Yulia Ikhsani, and Umairah Rizkya Gurning. "Perbandingan K-Means K-Medoids dan Fuzzy C-Means untuk Pengelompokan Data Pelanggan dengan Model LRFM." MALCOM: Indonesian Journal of Machine Learning and Computer Science 2, no. 2 (2022): 76–83. http://dx.doi.org/10.57152/malcom.v2i2.442.

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Indonesia memiliki pasar yang potensial untuk perusahaan kosmetik karena memiliki jumlah penduduk yang berjumlah hampir 270 juta jiwa. Pertumbuhan industri kosmetik di Indonesia mengalami perkembangan yang pesat dengan persentase pertumbuhan 5,59% pada bulan agustus 2021 silam. Dengan pertumbuhan tersebut perusahaan kosmetik memiliki reseller yang tersebar diseluruh daerah Indonesia. Penelitian ini menggunakan data pelanggan dari salah satu reseller perusahaan kecantikan. Pelanggan mana yang sering berbelanja, produk mana yang sering mereka beli, dan klien mana yang paling setia adalah masalah

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Rosyani, Perani, A. Suhendi, D. H. Apriyanti, and A. A. Waskita. "Color Features Based Flower Image Segmentation Using K-Means and Fuzzy C-Means." Building of Informatics, Technology and Science (BITS) 3, no. 3 (2021): 253–59. http://dx.doi.org/10.47065/bits.v3i3.1060.

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A more detail investigation of color feature for flower segmentation using K-means and fuzzy C-means was conducted in this paper. The sample images containing 1, 2, 3, 4 dianthus del- toides L flowers, obtained from ImageCLEF 2017 will be used. K-means and fuzzy C-means will use different color model components as the feature for segmenting the flower objects from their background while keeping the value of k for K-means and fuzzy C-means constant. Then the performance of the segmentation approaches will be evaluated by using the ground truth infor- mation. The evaluation parameters involved a

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Gupta, Vuddagiri Mutya Naga Sri Surya Venkata Krishna Rao, Chitta Venkata Phani Krishna, Konakanchi Venkata Subrahmanya Srirama Murthy, and Reddy Shiva Shankar. "Validation on selected breast cancer drugs of physicochemical features by using machine learning models." International Journal of Public Health Science (IJPHS) 13, no. 2 (2025): 794–803. https://doi.org/10.11591/ijphs.v13i2.23322.

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Breast cancer is one of the leading causes of death among females today. The elbow approach determines the ideal number of clusters after determining that the Dataset is highly cluster able with the Hopkins statistic. Three distinct groups with distinct differences were produced using the dataset's proposed expectation maximization fuzzy k-means clustering algorithm (PEMFKM). Different fuzzy clustering techniques, such as fuzzy k-means (FKM), fuzzy k-means with entropy (FKM.ENT), fuzzy k-means with entropy and noise (FKM.ENT.NOISE), Gustafson and Kessel - like fuzzy k-means (FKM.GK), Gustafson

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Sivarathri, Srinivas, and Govardhan A. "Experiments on Hypothesis "Fuzzy K-Means is Better than K-Means for Clustering"." International Journal of Data Mining & Knowledge Management Process 4, no. 5 (2014): 21–34. http://dx.doi.org/10.5121/ijdkp.2014.4502.

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Honda, K., A. Notsu, and H. Ichihashi. "Fuzzy PCA-Guided Robust $k$-Means Clustering." IEEE Transactions on Fuzzy Systems 18, no. 1 (2010): 67–79. http://dx.doi.org/10.1109/tfuzz.2009.2036603.

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Chen, Na, Ze-shui Xu, and Mei-mei Xia. "Hierarchical hesitant fuzzy K-means clustering algorithm." Applied Mathematics-A Journal of Chinese Universities 29, no. 1 (2014): 1–17. http://dx.doi.org/10.1007/s11766-014-3091-8.

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Coppi, Renato, and Pierpaolo D'Urso. "Fuzzy K-means clustering models for triangular fuzzy time trajectories." Statistical Methods & Applications 11, no. 1 (2002): 21–40. http://dx.doi.org/10.1007/s102600200022.

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Coppi, Renato, and Pierpaolo D'Urso. "Fuzzy K-means clustering models for triangular fuzzy time trajectories." Statistical Methods & Applications 11, no. 1 (2002): 21–40. http://dx.doi.org/10.1007/bf02511444.

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BAYRAM, Muhammed Ali, and Cemil Közkurt. "Comparison of K-Means, Fuzzy C-Means and Fuzzy Logic Based Clustering Algorithms for Customer Segmentation." Aintelia Science Notes 3, no. 1 (2024): 29–40. https://doi.org/10.5281/zenodo.14556226.

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Customer segmentation is a critical tool for businesses to optimize their marketing strategies and increase customer loyalty. This study aims to segment customers based on their buying behavior using K-Means, Fuzzy C-Means (FCM) and Fuzzy Logic based clustering algorithms. While the K-Means algorithm divides the data into a certain number of clusters and determines a center for each cluster, the FCM algorithm allows data points to belong to more than one cluster with varying degrees of membership. Fuzzy Logic based clustering provides a more nuanced segmentation by using a system of fuzzy memb

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Herdian Andika, Tahta. "Pengenalan Pola Berbasis Segmentasi Citra Menggunakan Algoritma Fuzzy C-Means Dan K-Means." Aisyah Journal Of Informatics and Electrical Engineering (A.J.I.E.E) 1, no. 1 (2019): 1–10. http://dx.doi.org/10.30604/jti.v1i1.3.

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Segmentasi merupakan salah satu bagian penting dalam analisis citra, karena pada prosedur ini gambar/citra yang diinginkan akan dianalisis untuk proses yang lebih lanjut agar lebih mudah di analisis gunat ujuan selanjutnya, misalnya pada pengenalan pola.Segmentasi citra yang merupakan bagian dari analisis citra digunakan untuk membagi sebuah citra menjadi beberapa bagian dan mengambil sebagian objek yang diinginkan.Salah satu teknik dalam segmentasi citra adalah dengan clustering. Clustering adalah suatu usaha untuk melakukan pengelompokan data berdasarkan kelas dan merupakan metode mengelompo

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W, Winarno. "Comparison Of Clustering Levels Of The Learning Burnout Of Students Using The Fuzzy C-Means And K-Means Methods." Jurnal Teknologi Informasi dan Pendidikan 16, no. 1 (2023): 38–53. http://dx.doi.org/10.24036/jtip.v16i1.668.

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Learning burnout is an impact from work done in a manner Keep going continuously, causing fatigue physical and emotional. If boredom study no handled, got cause students no productive and inhibits potency student . So from that study this proposed method clustering for group level saturation study students. The clustering process in research this use Fuzzy C-Means and K-Means. According to the previous study, Fuzzy C-Means and K-Means can produce results in the best clusters. Destination of study this is to compare performance from method Fuzzy C-Means and K-Means. The dataset used in this stu

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Gupta, Vuddagiri MNSSVKR, Chitta Venkata Phani Krishna, Konakanchi Venkata Subrahmanya Srirama Murthy, and Reddy Shiva Shankar. "Validation on selected breast cancer drugs of physicochemical features by using machine learning models." International Journal of Public Health Science (IJPHS) 13, no. 2 (2024): 794. http://dx.doi.org/10.11591/ijphs.v13i2.23322.

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Breast cancer is one of the leading causes of death among females today. The elbow approach determines the ideal number of clusters after determining that the Dataset is highly cluster able with the Hopkins statistic. Three distinct groups with distinct differences were produced using the dataset's proposed expectation maximization fuzzy k-means clustering algorithm (PEMFKM). Different fuzzy clustering techniques, such as fuzzy k-means (FKM), fuzzy k-means with entropy (FKM.ENT), fuzzy k-means with entropy and noise (FKM.ENT.NOISE), Gustafson and Kessel - like fuzzy k-means (FKM.GK), Gustafson

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Inna Auliya, Fadhilah Fitri, Nonong Amalita, and Tessy Octavia Mukhti. "Comparison of K-Means and Fuzzy C-Means Algorithms for Clustering Based on Happiness Index Components Across Provinces in Indonesia." UNP Journal of Statistics and Data Science 2, no. 1 (2024): 114–21. http://dx.doi.org/10.24036/ujsds/vol2-iss1/150.

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Cluster analysis is a multivariate technique aimed at grouping objects into several clusters based on the characteristics they possess. This study aims to determine the clustering results of 34 provinces in Indonesia based on the indicators of the happiness index for the year 2021 by comparing non-hierarchical cluster analysis methods, namely K-Means and Fuzzy C-Means. K-Means is a non-hierarchical cluster analysis that divides objects into cluster groups based on the distance of objects to the nearest cluster center, while Fuzzy C-Means is a cluster analysis that uses a fuzzy grouping model w

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Lestari, Indah, Ashari Mahfud, Edris Zamroni, Sucipto Sucipto, and Anisatul Latifah. "Comparison of Fairness Conditions Comparison Study with Fuzzy C-Means and K-Means Methods." QALAMUNA: Jurnal Pendidikan, Sosial, dan Agama 16, no. 1 (2024): 429–38. http://dx.doi.org/10.37680/qalamuna.v16i1.4992.

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The development of the character of fairness in teachers is influenced by the environmental conditions in which they grow and develop. This can be a factor that can influence the development of the fair character of teachers in the provinces of Central Java and Lampung. Therefore, this research aims to explore the fairness conditions among teachers in Central Java and Lampung provinces through a comparative study. This research involved 970 teachers spread across the islands of Java and Sumatra. The cluster sampling technique was used to take research subjects. The Comparison research method w

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Malik, Rio Andika, Sarjon Defit, and Yuhandri Yuhandri. "Comparison of K-Means Clustering Algorithm with Fuzzy C-Means In Measuring Satisfaction Level Of Television Da'wah Surau TV." Rabit : Jurnal Teknologi dan Sistem Informasi Univrab 3, no. 1 (2018): 10–21. http://dx.doi.org/10.36341/rabit.v3i1.387.

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Da'wah Television Surau TV is a broadcasting media that presents broadcasts around Islam. This media will quickly develop as it presents broadcasting material in meeting the spiritual needs of its viewers. To Increased media development is highly dependent on the satisfaction of the audience in all aspects of broadcast supporting. It is therefore, to measure the level of audience satisfaction as an effort to generate continuous broadcast quality improvement.This research is performing of algorithm clustering comparation with K-Means Clustering modeling and Fuzzy C-Means modeling to classify an

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Meng, Wang, Dui Hongyan, Zhou Shiyuan, Dong Zhankui, and Wu Zige. "The Kernel Rough K-Means Algorithm." Recent Advances in Computer Science and Communications 13, no. 2 (2020): 234–39. http://dx.doi.org/10.2174/2213275912666190716121431.

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Background: Clustering is one of the most important data mining methods. The k-means (c-means ) and its derivative methods are the hotspot in the field of clustering research in recent years. The clustering method can be divided into two categories according to the uncertainty, which are hard clustering and soft clustering. The Hard C-Means clustering (HCM) belongs to hard clustering while the Fuzzy C-Means clustering (FCM) belongs to soft clustering in the field of k-means clustering research respectively. The linearly separable problem is a big challenge to clustering and classification algo

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Bangura, R. M., S. D. Johnson, and O. Mbulayi. "Application of K-Means and Fuzzy K-Means to Rice Dataset in Sierra Leone." Sri Lankan Journal of Applied Statistics 21, no. 3 (2020): 69. http://dx.doi.org/10.4038/sljastats.v21i3.8062.

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Uperiati, Alena, Martaleli Bettiza, and Atika Puspasari. "PERBANDINGAN METODE FUZZY C-MEANS DAN K-MEANS DALAM KLASIFIKASI KELULUSAN MAHASISWA (STUDI KASUS : JURUSAN MANAJEMEN, UNIVERSITAS MARITIM RAJA ALI HAJI." Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan 9, no. 2 (2020): 75–81. http://dx.doi.org/10.31629/sustainable.v9i2.1409.

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Ketidakseimbangan antara jumlah mahasiswa yang masuk dan keluar menyebabkan penumpukan jumlah mahasiwa, dimana mahasiswa masuk dalam jumlah besar namun jumlah yang lulus tepat waktu jumlahnya jauh lebih kecil. Oleh karena itu, pada penelitian ini dilakukan guna untuk mengklasifikasikan kelulusan mahasiswa mengunakan metode fuzzy c-means dan k-means. Perbandingan metode fuzzy c-means dan k-means digunakan untuk mendapatkan hasil klasifikasi yang tepat dan akurat. Berdasarkan hasil pengujian yang telah dilakukan pada fuzzy c-means, parameter terbaik yang mengahasilkan hasil klasifikasi yang mend

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Drakopoulos, Georgios, Panagiotis Gourgaris, Andreas Kanavos, and Christos Makris. "A Fuzzy Graph Framework for Initializing k-Means." International Journal on Artificial Intelligence Tools 25, no. 06 (2016): 1650031. http://dx.doi.org/10.1142/s0218213016500317.

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k-Means is among the most significant clustering algorithms for vectors chosen from an underlying space S. Its applications span a broad range of fields including machine learning, image and signal processing, and Web mining. Since the introduction of k-Means, two of its major design parameters remain open to research. The first is the number of clusters to be formed and the second is the initial vectors. The latter is also inherently related to selecting a density measure for S. This article presents a two-step framework for estimating both parameters. First, the underlying vector space is re

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HONDA, Katsuhiro. "Robust k-Means Clustering and Fuzzy Principal Component Analysis." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 25, no. 3 (2013): 74–80. http://dx.doi.org/10.3156/jsoft.25.3_74.

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Dwitiyanti, Nurfidah, Siti Ayu Kumala, and Shinta Dwi Handayani. "Comparative Study of Earthquake Clustering in Indonesia Using K-Medoids, K-Means, DBSCAN, Fuzzy C-Means and K-AP Algorithms." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 8, no. 6 (2024): 768–78. https://doi.org/10.29207/resti.v8i6.5514.

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Indonesia’s frequent earthquakes, caused by its position at the convergence of multiple tectonic plates, Indonesia's frequent earthquakes, caused by its position at the convergence of multiple tectonic plates, necessitate precise seismic zone identification to improve disaster preparedness. This research evaluates the effectiveness of five clustering algorithms—K-Medoids, K-Means, DBSCAN, Fuzzy C-Means, and K-Affinity Propagation (K-AP)—for analyzing earthquake data from January 2017 to January 2023. Using a dataset from BMKG encompassing 13,860 seismic events, each algorithm was assessed base

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Belia Mailien, Admi Salma, Syafriandi, and Dina Fitria. "Comparison K-Means and Fuzzy C-Means Methods to Grouping Human Development Index Indicators in Indonesia." UNP Journal of Statistics and Data Science 1, no. 1 (2023): 23–30. http://dx.doi.org/10.24036/ujsds/vol1-iss1/4.

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The Human Development Index (HDI) is an important indicator to measure the success of efforts to improve people's quality of life. The increase in the human development index in Indonesia is not accompanied by an even distribution of the human development index in every district/city in Indonesia. To facilitate the government in making policies and plans in overcoming the uneven HDI in Indonesia, it is necessary to group districts/cities in Indonesia based on HDI indicators. This study discusses the use of the K-means and Fuzzy C-Means algorithms with a total of 4 clusters. The grouping result

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Tanjung, Tia, Fenty Ariani, Wiwin Susanty, and Arnes Yuli Vandika. "Perhitungan Estimasi Upaya Pengembangan Software Pulsa Online dengan Fuzzy C-Means dan Fuzzy K-Means." EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi 12, no. 1 (2022): 49. http://dx.doi.org/10.36448/expert.v12i1.2471.

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Wa, Ode Ermalianti, Pramono Bambang, and Ransi Natalis. "SISTEM PENDUKUNG KEPUTUSAN PENENTUAN PENERIMA BANTUAN BEDAH RUMAH MENGGUNAKAN METODE CLUSTERING K- MEANS DAN FUZZY ANALITICAL HIERARCHY PROCESS." semanTIK Volume 7 No 2 Jul-Des 2021 (December 25, 2021): 133–40. https://doi.org/10.5281/zenodo.5791704.

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Dinas Perumahan Kawasan Permukiman dan Pertanahan Kota Kendari merupakan instansi yang menyediakan beberapa bantuan kepada masyarakat untuk meningkatkan kesejahteraannya yaitu program Bantuan Stimulan Perumahan Swadaya (BSPS). Kriteria penerima bantuan bedah rumah dilihat dari kondisi bangunan rumah seperti kondisi atap, kondisi lantai, kondisi dinding, penghasilan kepala rumah tangga, jumlah tanggungan, pekerjaan, luas rumah dan bukti kepemilikan tanah sebagai syarat penerima bantuan. Dalam penelitian ini digunakan metode <em>clustering</em> <em>K-Means </em>dan <em>Fuzzy Analitical Hierarchy

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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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Rosyid, Harunur, Muhammad Modi Bin Lakulu, and Ramlah Bt. Mailok. "Hybrid ABC–K Means for Optimal Cluster Number Determination in Unlabeled Data." Mobile and Forensics 6, no. 2 (2024): 52–65. http://dx.doi.org/10.12928/mf.v6i2.11529.

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This study presents the ABC K Means GenData algorithm, an enhancement over traditional K Means clustering that integrates the Artificial Bee Colony (ABC) optimization approach. The ABC K Means GenData algorithm addresses the issue of local optima commonly encountered in standard K Means algorithms, offering improved exploration and exploitation strategies. By utilizing the dynamic roles of employed, onlooker, and scout bees, this approach effectively navigates the clustering space for categorical data. Performance evaluations across several datasets demonstrate the algorithm's superiority. For

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Ilhan, Sevinc, Nevcihan Duru, and Esref Adali. "Improved Fuzzy Art Method for Initializing K-means." International Journal of Computational Intelligence Systems 3, no. 3 (2010): 274. http://dx.doi.org/10.2991/ijcis.2010.3.3.3.

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Lasek, Piotr, Wojciech Rząsa, and Anna Król. "Aggregations of Fuzzy Equivalences in k-means Algorithm." Procedia Computer Science 246 (2024): 830–39. http://dx.doi.org/10.1016/j.procs.2024.09.502.

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Aswani Kumar, Ch, and S. Srinivas. "Concept lattice reduction using fuzzy K-Means clustering." Expert Systems with Applications 37, no. 3 (2010): 2696–704. http://dx.doi.org/10.1016/j.eswa.2009.09.026.

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Ilhan, Sevinc, Nevcihan Duru, and Esref Adali. "Improved Fuzzy Art Method for Initializing K-means." International Journal of Computational Intelligence Systems 3, no. 3 (2010): 274–79. http://dx.doi.org/10.1080/18756891.2010.9727698.

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Karlekar, Aditya, Ayan Seal, Ondrej Krejcar, and Consuelo Gonzalo-Martin. "Fuzzy K-Means Using Non-Linear S-Distance." IEEE Access 7 (2019): 55121–31. http://dx.doi.org/10.1109/access.2019.2910195.

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Blömer, Johannes, Sascha Brauer, and Kathrin Bujna. "A Complexity Theoretical Study of Fuzzy K -Means." ACM Transactions on Algorithms 16, no. 4 (2020): 1–25. http://dx.doi.org/10.1145/3409385.

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Wu, Xiaohong, Bin Wu, Jun Sun, Shengwei Qiu, and Xiang Li. "A hybrid fuzzy K-harmonic means clustering algorithm." Applied Mathematical Modelling 39, no. 12 (2015): 3398–409. http://dx.doi.org/10.1016/j.apm.2014.11.041.

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., Adriyendi. "Clustering using K-Means and Fuzzy C-Means on Food Productivity." International Journal of u- and e- Service, Science and Technology 9, no. 12 (2016): 291–308. http://dx.doi.org/10.14257/ijunesst.2016.9.12.26.

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Mr., Mohan Raj C. S., and Srikanth V. Dr. "K-Means and Fuzzy C-Means Algorithm for Mammogramy Image Segmentation." Sangrathan Journal, UGC Care Listed Journal 4, no. 1 (2024): 203–15. https://doi.org/10.5281/zenodo.11000974.

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One of the foremost challenges in image analysis is image segmentation. The majority of medical applications often involve trained operators extracting images from targeted regions that may be physically distinct but statistically indistinguishable. Also, Image segmentation is time-consuming and has poor reproducibility often subjected to manual errors and biases. Identification of clusters in given data is another challenge during clustering. K-means is a widely used clustering technique that divides the data into K different clusters. In this strategy, clusters are specified in advance, whic

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Praja, Abdi, Chairisni Lubis, and Dyah Erny Herwindiati. "DETEKSI PENYAKIT DIABETES DENGAN METODE FUZZY C-MEANS CLUSTERING DAN K-MEANS CLUSTERING." Computatio : Journal of Computer Science and Information Systems 1, no. 1 (2017): 15. http://dx.doi.org/10.24912/computatio.v1i1.233.

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Diabetes adalah penyakit yang terjadi ketika kandungan glukosa di dalam darah tinggi. Tes glukosa yang menghasilkan keakuratan tinggi harus dilakukan beberapa kali untuk mendeteksi diabetes di dalam tubuh. Beberapa indikator di dalam tubuh dapat menjadi titik awal untuk mendeteksi diabetes. Bagaimanapun juga, keterbatasan seorang tenaga medis dalam mendeteksi dalam jumlah data yang sangat besar dengan cara manual menjadi kendala. Salah satu solusi untuk gap tersebut adalah menggunakan komputer sebagai perhitungan matematika dalam metode pengelompokan K-Means dan Fuzzy C-Means. Pengelompokan te

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