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Journal articles on the topic 'Fuzzy Possibilistic C-Means'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Putri, Ghina Nabila Saputro, Dwi Ispriyanti, and Tatik Widiharih. "IMPLEMENTASI ALGORITMA FUZZY C-MEANS DAN FUZZY POSSIBILISTICS C-MEANS UNTUK KLASTERISASI DATA TWEETS PADA AKUN TWITTER TOKOPEDIA." Jurnal Gaussian 11, no. 1 (2022): 86–98. http://dx.doi.org/10.14710/j.gauss.v11i1.33996.

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Social media has become the most popular media, which can be accessed by young to old age. Twitter became one of the effective media and the familiar one used by the public, thus making the company make Twitter one of the promotional tools, one of which is Tokopedia. The research aims to group tweets uploaded by @tokopedia Twitter accounts based on the type of tweets content that gets a lot of retweets and likes by followers of @tokopedia. Application of text mining to cluster tweets on the @tokopedia Twitter account using Fuzzy C-Means and Fuzzy Possibilistic C-Means algorithms that viewed th

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Pimentel, Bruno Almeida, and Renata M. C. R. de Souza. "A Generalized Multivariate Approach for Possibilistic Fuzzy C-Means Clustering." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26, no. 06 (2018): 893–916. http://dx.doi.org/10.1142/s021848851850040x.

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Fuzzy c-Means (FCM) and Possibilistic c-Means (PCM) are the most popular algorithms of the fuzzy and possibilistic clustering approaches, respectively. A hybridization of these methods, called Possibilistic Fuzzy c-Means (PFCM), solves noise sensitivity defect of FCM and overcomes the coincident clusters problem of PCM. Although PFCM have shown good performance in cluster detection, it does not consider that different variables can produce different membership and possibility degrees and this can improve the clustering quality as it has been performed with the Multivariate Fuzzy c-Means (MFCM)

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Naghi, Mirtill-Boglárka, Levente Kovács, and László Szilágyi. "A generalized fuzzy-possibilistic c-means clustering algorithm." Acta Universitatis Sapientiae, Informatica 15, no. 2 (2023): 404–31. http://dx.doi.org/10.2478/ausi-2023-0023.

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Abstract The so-called fuzzy-possibilistic c-means (FPCM) algorithm was introduced as an early mixed-partition method aiming to eliminate some adverse effects present in the behavior of the fuzzy c-means (FCM) and the possibilistic c-means (PCM) algorithms. A great advantage of FPCM was the low number of its parameters, as it eliminated the possibilistic penalty terms used by PCM. Unfortunately, FPCM in its original formulation also has a weak point: the strength of the possibilistic term is in inverse proportion with the number of clustered data items, which makes FPCM act like FCM when clust

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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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El Harchaoui, Nour-Eddine, Mounir Ait Kerroum, Ahmed Hammouch, Mohamed Ouadou, and Driss Aboutajdine. "Unsupervised Approach Data Analysis Based on Fuzzy Possibilistic Clustering: Application to Medical Image MRI." Computational Intelligence and Neuroscience 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/435497.

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The analysis and processing of large data are a challenge for researchers. Several approaches have been used to model these complex data, and they are based on some mathematical theories: fuzzy, probabilistic, possibilistic, and evidence theories. In this work, we propose a new unsupervised classification approach that combines the fuzzy and possibilistic theories; our purpose is to overcome the problems of uncertain data in complex systems. We used the membership function of fuzzy c-means (FCM) to initialize the parameters of possibilistic c-means (PCM), in order to solve the problem of coinc

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Wan, Renxia, Yuelin Gao, and Caixia Li. "Weighted Fuzzy-Possibilistic C-Means Over Large Data Sets." International Journal of Data Warehousing and Mining 8, no. 4 (2012): 82–107. http://dx.doi.org/10.4018/jdwm.2012100104.

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Up to now, several algorithms for clustering large data sets have been presented. Most clustering approaches for data sets are the crisp ones, which cannot be well suitable to the fuzzy case. In this paper, the authors explore a single pass approach to fuzzy possibilistic clustering over large data set. The basic idea of the proposed approach (weighted fuzzy-possibilistic c-means, WFPCM) is to use a modified possibilistic c-means (PCM) algorithm to cluster the weighted data points and centroids with one data segment as a unit. Experimental results on both synthetic and real data sets show that

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Aghyari, Ghia Fauziah, and Abdul Kudus. "Penerapan Algoritma Fuzzy Possibilistic C-Means (FPCM) pada Pengelompokan Kabupaten/Kota di Indonesia Berdasarkan Indikator Indeks Pembangunan Manusia Tahun 2022." Bandung Conference Series: Statistics 3, no. 2 (2023): 113–20. http://dx.doi.org/10.29313/bcss.v3i2.7321.

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Abstract. Human resources are a crucial factor in human development and a key component in achieving prosperity in every country. The success of development is measured in various ways, one of the most popular being the calculation of the Human Development Index (HDI). The classification of districts and cities in Indonesia is necessary as a reference for government program planning and evaluation to enhance human development in those areas. Partitioning clustering is one of the clustering techniques that aims to partition data into several groups or partitions, with the number of groups usual

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Chowdhary, Chiranji Lal, and D. P. Acharjya. "Clustering Algorithm in Possibilistic Exponential Fuzzy C-Mean Segmenting Medical Images." Journal of Biomimetics, Biomaterials and Biomedical Engineering 30 (January 2017): 12–23. http://dx.doi.org/10.4028/www.scientific.net/jbbbe.30.12.

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Different fuzzy segmentation methods were used in medical imaging from last two decades for obtaining better accuracy in various approaches like detecting tumours etc. Well-known fuzzy segmentations like fuzzy c-means (FCM) assign data to every cluster but that is not realistic in few circumstances. Our paper proposes a novel possibilistic exponential fuzzy c-means (PEFCM) clustering algorithm for segmenting medical images. This new clustering algorithm technology can maintain the advantages of a possibilistic fuzzy c-means (PFCM) and exponential fuzzy c-mean (EFCM) clustering algorithms to ma

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Azzouzi, Souad, Amal Hjouji, Jaouad EL- Mekkaoui, and Ahmed EL Khalfi. "A Generalization of Possibilistic Fuzzy C-Means Method for Statistical Clustering of Data." International Journal of Circuits, Systems and Signal Processing 15 (December 17, 2021): 1766–80. http://dx.doi.org/10.46300/9106.2021.15.191.

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The Fuzzy C-means (FCM) algorithm has been widely used in the field of clustering and classification but has encountered difficulties with noisy data and outliers. Other versions of algorithms related to possibilistic theory have given good results, such as Fuzzy C- Means(FCM), possibilistic C-means (PCM), Fuzzy possibilistic C-means (FPCM) and possibilistic fuzzy C- Means algorithm (PFCM).This last algorithm works effectively in some environments but encountered more shortcomings with noisy databases. To solve this problem, we propose in this manuscript, a new algorithm named Improved Possibi

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Chaudhuri, Arindam. "Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms." Advances in Fuzzy Systems 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/238237.

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Intuitionistic fuzzy sets (IFSs) provide mathematical framework based on fuzzy sets to describe vagueness in data. It finds interesting and promising applications in different domains. Here, we develop an intuitionistic fuzzy possibilistic C means (IFPCM) algorithm to cluster IFSs by hybridizing concepts of FPCM, IFSs, and distance measures. IFPCM resolves inherent problems encountered with information regarding membership values of objects to each cluster by generalizing membership and nonmembership with hesitancy degree. The algorithm is extended for clustering interval valued intuitionistic

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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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Pal, N. R., K. Pal, J. M. Keller, and J. C. Bezdek. "A possibilistic fuzzy c-means clustering algorithm." IEEE Transactions on Fuzzy Systems 13, no. 4 (2005): 517–30. http://dx.doi.org/10.1109/tfuzz.2004.840099.

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Chen, Jiashun, Hao Zhang, Dechang Pi, Mehmed Kantardzic, Qi Yin, and Xin Liu. "A Weight Possibilistic Fuzzy C-Means Clustering Algorithm." Scientific Programming 2021 (June 10, 2021): 1–10. http://dx.doi.org/10.1155/2021/9965813.

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Fuzzy C-means (FCM) is an important clustering algorithm with broad applications such as retail market data analysis, network monitoring, web usage mining, and stock market prediction. Especially, parameters in FCM have influence on clustering results. However, a lot of FCM algorithm did not solve the problem, that is, how to set parameters. In this study, we present a kind of method for computing parameters values according to role of parameters in the clustering process. New parameters are assigned to membership and typicality so as to modify objective function, on the basis of which Lagrang

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Szilágyi, László, Szidónia Lefkovits, and Sándor M. Szilágyi. "Self-Tuning Possibilistic c-Means Clustering Models." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 27, Supp01 (2019): 143–59. http://dx.doi.org/10.1142/s0218488519400075.

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The relaxation of the probabilistic constraint of the fuzzy c-means clustering model was proposed to provide robust algorithms that are insensitive to strong noise and outlier data. These goals were achieved by the possibilistic c-means (PCM) algorithm, but these advantages came together with a sensitivity to cluster prototype initialization. According to the original recommendations, the probabilistic fuzzy c-means (FCM) algorithm should be applied to establish the cluster initialization and possibilistic penalty terms for PCM. However, when FCM fails to provide valid cluster prototypes due t

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Dubey, Yogita, Milind Mushrif, and Kajal Mitra. "BRAIN TUMOR DETECTION AND SEGMENTATION USING MULTISCALE INTUITIONISTIC FUZZY ROUGHNESS IN MR IMAGES." Biomedical Engineering: Applications, Basis and Communications 31, no. 03 (2019): 1950020. http://dx.doi.org/10.4015/s1016237219500200.

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The magnetic resonance imaging technique is mostly used for visualizing and detecting brain tumor, which requires accurate segmentation of brain MR images into white matter, gray matter, cerebrospinal fluid, necrotic tissue, tumor, and edema. But brain image segmentation is a challenging task because of unknown noise and intensity inhomogeneity in brain MR images. This paper proposed a technique for the segmentation and the detection of a tumor, cystic component and edema in brain MR images using multiscale intuitionistic fuzzy roughness (MSIFR). Application of linear scale-space theory and in

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Bouzbida, Mohamed, Lassad Hassine, and Abdelkader Chaari. "Robust Kernel Clustering Algorithm for Nonlinear System Identification." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/2427309.

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In engineering field, it is necessary to know the model of the real nonlinear systems to ensure its control and supervision; in this context, fuzzy modeling and especially the Takagi-Sugeno fuzzy model has drawn the attention of several researchers in recent decades owing to their potential to approximate nonlinear behavior. To identify the parameters of Takagi-Sugeno fuzzy model several clustering algorithms are developed such as the Fuzzy C-Means (FCM) algorithm, Possibilistic C-Means (PCM) algorithm, and Possibilistic Fuzzy C-Means (PFCM) algorithm. This paper presents a new clustering algo

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Verma, Hanuman, and R. K. Agrawal. "Possibilistic Intuitionistic Fuzzy c-Means Clustering Algorithm for MRI Brain Image Segmentation." International Journal on Artificial Intelligence Tools 24, no. 05 (2015): 1550016. http://dx.doi.org/10.1142/s0218213015500165.

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Accurate segmentation of human brain image is an essential step for clinical study of magnetic resonance imaging (MRI) images. However, vagueness and other ambiguity present between the brain tissues boundaries can lead to improper segmentation. Possibilistic fuzzy c-means (PFCM) algorithm is the hybridization of fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms which overcomes the problem of noise in the FCM algorithm and coincident clusters problem in the PCM algorithm. A major challenge posed in the PFCM algorithm for segmentation of ill-defined MRI image with noise is to take

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Gurler, Orkun. "COMPREHENSIVE ANALYSIS OF FUZZY C - MEANS CLUSTERING AND ITS VARIANTS." Journal of Modern Technology and Engineering 9, no. 1 (2024): 69–93. http://dx.doi.org/10.62476/jmte9269.

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Fuzzy clustering algorithms are one of the most important techniques for analysing and extracting information from data when working with datasets containing overlapping clusters. Fuzzy clustering provides a more precise representation of complex data structures compared to conventional crisp (hard) clustering approaches. It accomplishes this by allowing data points to be assigned to multiple clusters with different degrees of membership. This paper provides an extensive review of various fuzzy clustering algorithms, such as Fuzzy C - Means (FCM) and its variations including Gustafson - Kessel

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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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Thiyagarajan, V. S., and Venkatachalapathy Venkatachalapathy. "Privacy Preserving Probabilistic Possibilistic Fuzzy C Means Clustering." Research Journal of Applied Sciences, Engineering and Technology 11, no. 1 (2015): 27–39. http://dx.doi.org/10.19026/rjaset.11.1672.

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Ji, Zexuan, Yong Xia, Quansen Sun, and Guo Cao. "Interval-valued possibilistic fuzzy C-means clustering algorithm." Fuzzy Sets and Systems 253 (October 2014): 138–56. http://dx.doi.org/10.1016/j.fss.2013.12.011.

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Purba, Feronika Paska, Klause Roder, Elmanani Simamora, and Hamidah Nasution. "Implementation off Fuzzy C-Means (FCM) and Fuzzy Possibilistic C-Means (FPCM) for Clustering District/City Based on Health Services and Infectious Diseases in North Sumatera." ZERO: Jurnal Sains, Matematika dan Terapan 8, no. 2 (2025): 47. https://doi.org/10.30829/zero.v8i2.23480.

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<p>The study aims to compare the Fuzzy C-Means (FCM) and Fuzzy Possibilistic C-Means (FPCM) algorithms and to profile the results of district / city clusters in North Sumatra based on health services and infectious disease sufferers. The method used is descriptive quantitative research using annual data obtained from the North Sumatra Provincial Health Office for the period 2023. The data was collected and then analyzed using both Clustering algorithms to find the most optimal results. The results showed that Fuzzy Possibilistic C-Means proved to be a better algorithm compared to Fuzzy C

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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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Rustam, Koredianto Usman, Mudyawati Kamaruddin, et al. "MODIFIED POSSIBILISTIC FUZZY C-MEANS ALGORITHM FOR CLUSTERING INCOMPLETE DATA SETS." Acta Polytechnica 61, no. 2 (2021): 364–77. http://dx.doi.org/10.14311/ap.2021.61.0364.

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A possibilistic fuzzy c-means (PFCM) algorithm is a reliable algorithm proposed to deal with the weaknesses associated with handling noise sensitivity and coincidence clusters in fuzzy c-means (FCM) and possibilistic c-means (PCM). However, the PFCM algorithm is only applicable to complete data sets. Therefore, this research modified the PFCM for clustering incomplete data sets to OCSPFCM and NPSPFCM with the performance evaluated based on three aspects, 1) accuracy percentage, 2) the number of iterations, and 3) centroid errors. The results showed that the NPSPFCM outperforms the OCSPFCM with

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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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Lestari, Mega, Dwi Kartini, Irwan Budiman, Mohammad Reza Faisal, and Muliadi Muliadi. "Comparison of Industrial Business Grouping Using Fuzzy C-Means and Fuzzy Possibilistic C-Means Methods." Telematika 16, no. 2 (2023): 91–102. http://dx.doi.org/10.35671/telematika.v16i2.2548.

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The industrial business sector plays a role in the development of the economic sector in developing countries such as Indonesia. In this case, many industrial businesses are growing, but the data has not been processed or analyzed to produce important information that can be processed into knowledge using data mining. One of the data mining techniques used in this research is data grouping, or clustering. This research was conducted to determine the comparison results of the Cluster Validity Index on Fuzzy C-Means and Fuzzy Possibilistic C-Means methods for clustering industrial businesses in

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Boudouda, Houria, Mohamed Nemissi, Hamid Seridi, and Herman Akdag. "Fuzzy-Possibilistic Classification: Resolution of Initialization Problem." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 1 (2009): 45–51. http://dx.doi.org/10.20965/jaciii.2009.p0045.

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The methods of automatic classification resulting from the artificial intelligence are generally the consequences of a formalism based on an artificial reasoning quasi similar to that of the human expert. All the approaches of automatic classification developed so far, whether in an exact or approximate context, are dissociated from each other by the membership concept of an object to a class. In this paper, we present a new approach hybrid of unsupervised automatic classification under the C-Means (means of C classes) family. This new approach, based on the fusion of fuzzy and possibility the

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Shyla, S. Immaculate, and S. S. Sujatha. "An Efficient Automatic Intrusion Detection in Cloud Using Optimized Fuzzy Inference System." International Journal of Information Security and Privacy 14, no. 4 (2020): 22–41. http://dx.doi.org/10.4018/ijisp.2020100102.

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Security incidents such as denial of service (DoS), scanning, malware code injection, viruses, worms, and password cracking are becoming common in a cloud environment that affects companies and may produce a financial loss if not detected in time. Such problems are handled by presenting an intrusion detection system (IDS) into the cloud. The existing cloud IDSs affect low detection accuracy, high false detection rate, and execution time. To overcome this problem, in this article, a gravitational search algorithm-based fuzzy inference system (GSA-FIS) is developed as intrusion detection. In thi

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Kurniasari, Dian, Virda Kurniawati, Aang Nuryaman, Mustofa Usman, and Rizki Khoirun Nisa. "IMPLEMENTATION OF FUZZY C-MEANS AND FUZZY POSSIBILISTIC C-MEANS ALGORITHMS ON POVERTY DATA IN INDONESIA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 18, no. 3 (2024): 1919–30. http://dx.doi.org/10.30598/barekengvol18iss3pp1919-1930.

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Cluster analysis involves the methodical categorization of data based on the degree of similarity within each group to group data with similar characteristics. This study focuses on classifying poverty data across Indonesian provinces. The methodologies employed include the Fuzzy C-Means (FCM) and Fuzzy Probabilistic C-Means (FPCM) algorithms. The FCM algorithm is a clustering approach where membership values determine the presence of each data point in a cluster. On the other hand, the FPCM algorithm builds upon FCM and Possibilistic C (PCM) algorithms by incorporating probabilistic considera

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ZHOU, JIAN, and CHIH-CHENG HUNG. "A GENERALIZED APPROACH TO POSSIBILISTIC CLUSTERING ALGORITHMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, supp02 (2007): 117–38. http://dx.doi.org/10.1142/s0218488507004650.

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Fuzzy clustering is an approach using the fuzzy set theory as a tool for data grouping, which has advantages over traditional clustering in many applications. Many fuzzy clustering algorithms have been developed in the literature including fuzzy c-means and possibilistic clustering algorithms, which are all objective-function based methods. Different from the existing fuzzy clustering approaches, in this paper, a general approach of fuzzy clustering is initiated from a new point of view, in which the memberships are estimated directly according to the data information using the fuzzy set theor

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Mahfouz, Mohamed A. "PPS-FPCM: PRIVACY-PRESERVING SEMI-FUZZY POSSIBILISTIC C-MEANS." International Journal of Advanced Research in Computer Science 14, no. 03 (2023): 150–63. http://dx.doi.org/10.26483/ijarcs.v14i3.6991.

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Applying traditional clustering techniques to big data on the cloud while preserving the privacy of the data is a challenge due to the required division and exponential operations in each iteration, which complicate its implementation on encrypted data. Several existing approaches are based on approximating the formulas of centers, weights, and memberships as three polynomial functions according to the multivariate Taylor formula. However, they usually suffer an increase in complexity and a slight drop in accuracy. In this paper, a novel Privacy-Preserving semi-fuzzy clustering algorithm based

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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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Joo, Won-Hee, and Frank Chung-Hoon Rhee. "Determining the Fuzzifier Values for Interval Type-2 Possibilistic Fuzzy C-means Clustering." Journal of Korean Institute of Intelligent Systems 27, no. 2 (2017): 99–105. http://dx.doi.org/10.5391/jkiis.2017.27.2.099.

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Ahmed, Troudi, Bouzbida Mohamed, and Chaari Abdelkader. "Nonlinear System Identification Using Clustering Algorithm Based on Kernel Method and Particle Swarm Optimization." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 23, no. 05 (2015): 667–83. http://dx.doi.org/10.1142/s0218488515500294.

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Many clustering algorithms have been proposed in literature to identify the parameters involved in the Takagi–Sugeno fuzzy model, we can quote as an example the Fuzzy C-Means algorithm (FCM), the Possibilistic C-Means algorithm (PCM), the Allied Fuzzy C-Means algorithm (AFCM), the NEPCM algorithm and the KNEPCM algorithm. The main drawback of these algorithms is the sensitivity to initialization and the convergence to a local optimum of the objective function. In order to overcome these problems, the particle swarm optimization is proposed. Indeed, the particle swarm optimization is a global o

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Satriyanto, Edi, Ni Wayan Surya Wardhani, Syaiful Anam, and Wayan Firdaus Mahmudy. "Improved Fuzzy Possibilistic C-Means using Artificial Bee Colony for Clustering New Student’s Financial Capability to Determine Tuition Level." JOIV : International Journal on Informatics Visualization 8, no. 4 (2024): 2066. https://doi.org/10.62527/joiv.8.4.3087.

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Outliers in the dataset will affect the quality of the cluster, so a good clustering method is needed. Based on the Mahalanobis distance method, it is known that the research dataset has outliers. Clustering methods that are often used for this type of data are Fuzzy C-means (FCM), Possibilistic C-means (PCM), and Fuzzy Possibilistic C-means (FPCM). This study aims to develop a clustering method that is more robust to outliers by using the Artificial Bee Colony (ABC) algorithm to minimize the objective function of FPCM. This study produces a new algorithm called Artificial Bee Colony Fuzzy Pos

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Jabeur Telmoudi, Achraf, Moez Soltani, Lotfi Chaouech, and Abdelkader Chaari. "Parameter estimation of nonlinear systems using a robust possibilistic c-regression model algorithm." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 234, no. 1 (2018): 134–43. http://dx.doi.org/10.1177/0959651818756246.

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This article studies the problem of inappropriate parameter estimation for nonlinear system when the dataset is contaminated by noise based on fuzzy c-regression models. In comparison to the existing algorithms in the literature, the proposed method uses a generalized objective function that reduces the errors of partitioning datasets contaminated by noise, and as a consequence an accurate model is obtained. Indeed, it combines a modified version of possibilistic c-means procedure with fuzzy c-regression models. The weighted least squares method is exploited to identify the parameters containe

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Esaki, Tomohito, Tomonori Hashiyama, and Yahachiro Tsukamoto. "Fuzzy Clustering Based on Total Uncertainty Degree." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 8 (2007): 897–904. http://dx.doi.org/10.20965/jaciii.2007.p0897.

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Traditional Fuzzy c-Means (FCM) methods have probabilistic and additive restrictions that ∑ μ (x) = 1; the sum of membership values on the identified membership function is one. Possibilistic clustering methods identify membership functions without such constraints, but some parameters used in objective functions are difficult to understand and membership function shapes are independent of clusters estimated through possibilistic methods. We propose novel fuzzy clustering using a total uncertainty degree based on evidential theory with which we obtain nonadditive membership functions whose the

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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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Satriyanto, Edi, Ni Wayan Surya Wardhani, Syaiful Anam, and Wayan Firdaus Mahmudy. "Performance Comparison of Dimensional Reduction using Principal Component Analysis with Alternating Least Squares in Modified Fuzzy Possibilistic C-Means and Fuzzy Possibilistic C-Means." International Journal on Advanced Science, Engineering and Information Technology 14, no. 2 (2024): 483–91. http://dx.doi.org/10.18517/ijaseit.14.2.19911.

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The clustering method is said to be good if it has resistance to outlier data. One cluster method resistant to outlier data is Fuzzy Possibilistic C-Means (FPCM). FPCM performance on outlier data still has the potential for overlap between cluster members in different clusters, resulting in decreased cluster quality. The Modified Fuzzy Possibilistic C-Means (MFPCM) method is used to modify FPCM in its objective function by inserting updated weight values to increase FPCM performance. In this research, improving the quality of FPCM and MFPCM clusters was carried out by reducing data dimensions

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Miyamoto, Sadaaki, Youhei Kuroda, and Kenta Arai. "Algorithms for Sequential Extraction of Clusters by Possibilistic Method and Comparison with Mountain Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 5 (2008): 448–53. http://dx.doi.org/10.20965/jaciii.2008.p0448.

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In addition to fuzzy c-means, possibilistic clustering is useful because it is robust against noise in data. The generated clusters are, however, strongly dependent on an initial value. We propose a family of algorithms for sequentially generating clusters “one cluster at a time,” which includes possibilistic medoid clustering. These algorithms automatically determine the number of clusters. Due to possibilistic clustering's similarity to the mountain clustering by Yager and Filev, we compare their formulation and performance in numerical examples.

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SHIBUYA, Kazuhiro, Sadaaki MIYAMOTO, Osamu TAKATA, and Kazutaka UMAYAHARA. "Regularization and Constraints in Fuzzy c-Means and Possibilistic Clustering." Journal of Japan Society for Fuzzy Theory and Systems 13, no. 6 (2001): 707–15. http://dx.doi.org/10.3156/jfuzzy.13.6_707.

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Trupthi, Mandhula, Suresh Pabboju, and Gugulotu Narsimha. "Possibilistic Fuzzy C-means Topic Modelling for Twitter Sentiment Analysis." International Journal of Intelligent Engineering and Systems 11, no. 3 (2018): 100–108. http://dx.doi.org/10.22266/ijies2018.0630.11.

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Arora, Jyoti, and Meena Tushir. "A new kernel-based possibilistic intuitionistic fuzzy c-means clustering." International Journal of Artificial Intelligence and Soft Computing 6, no. 4 (2017): 306. http://dx.doi.org/10.1504/ijaisc.2017.10018304.

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Arora, Jyoti, and Meena Tushir. "A new kernel-based possibilistic intuitionistic fuzzy c-means clustering." International Journal of Artificial Intelligence and Soft Computing 6, no. 4 (2018): 306. http://dx.doi.org/10.1504/ijaisc.2018.097282.

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Kannan, S. R., R. Devi, S. Ramathilagam, and T. P. Hong. "Effective fuzzy possibilistic c-means: an analyzing cancer medical database." Soft Computing 21, no. 11 (2016): 2835–45. http://dx.doi.org/10.1007/s00500-016-2198-7.

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Hamasuna, Yukihiro, Yasunori Endo, and Sadaaki Miyamoto. "On tolerant fuzzy c-means clustering and tolerant possibilistic clustering." Soft Computing 14, no. 5 (2009): 487–94. http://dx.doi.org/10.1007/s00500-009-0451-z.

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A, Vaishnavi. "Effective Web personalization system using Modified Fuzzy Possibilistic C Means." Bonfring International Journal of Software Engineering and Soft Computing 1, no. 1 (2011): 01–07. http://dx.doi.org/10.9756/bijsesc.1001.

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Do Viet Duc, Ngo Thanh Long, Ha Trung Hai, Chu Van Hai, and Nghiem Van Tam. "A possibilistic Fuzzy c-means algorithm based on improved Cuckoo search for data clustering." Journal of Military Science and Technology, CSCE6 (December 30, 2022): 3–15. http://dx.doi.org/10.54939/1859-1043.j.mst.csce6.2022.3-15.

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Possibilistic Fuzzy c-means (PFCM) algorithm is a powerful clustering algorithm. It is a combination of two algorithms Fuzzy c-means (FCM) and Possibilistic c-means (PCM). PFCM algorithm deals with the weaknesses of FCM in handling noise sensitivity and the weaknesses of PCM in the case of coincidence clusters. However, PFCM still has a common weakness of clustering algorithms that is easy to fall into local optimization. Cuckoo search (CS) is a novel evolutionary algorithm, which has been tested on some optimization problems and proved to be stable and high-efficiency. In this study, we propo

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Nida, Khairun, Memi Nor Hayati, and Rito Goejantoro. "Implementasi Metode Fuzzy Possibilistic C-Means pada Pengelompokan Provinsi di Indonesia Berdasarkan Data Jumlah Kejadian dan Dampak Bencana Banjir." Journal of Mathematics Education and Science 7, no. 1 (2024): 33–42. http://dx.doi.org/10.32665/james.v7i1.1919.

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Analisis cluster merupakan salah satu teknik dalam data mining yang digunakan untuk menemukan kelompok objek data yang serupa. Metode Fuzzy Possibilistic C-Means (FPCM) adalah salah satu metode clustering yang merupakan pengembangan dari algoritma Fuzzy C-Means (FCM) dan Possibilistic C-Means (PCM) dengan menggunakan kelebihan dari pemodelan fuzzy dan possibilistic. Penelitian ini bertujuan untuk mengetahui jumlah cluster optimal berdasarkan indeks validitas Modified Partition Coefficient (MPC) serta mengetahui hasil pengelompokan optimal 34 Provinsi di Indonesia berdasarkan data jumlah kejadi

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KATALAY KAFUNDA, Pierre, and Félicien Jordan MASAKUNA. "Classification non supervisé flou basé avec régularisation dans la fonction cout pour la segmentation du syndrome métabolique." Journal Africain des Sciences 2, no. 1 (2025): 46–54. https://doi.org/10.70237/jafrisci.2025.v2.i1.05.

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Fuzzi C-Means is an unsupervised learning algorithm which aims to create fuzzy classes from a heterogeneous population. In the fuzzy framework, the classes obtained form an overlap and not a partition as in the deterministic case where the variables are known with certainty. In this article, our objective is to create a sof computing which processes data characterized by imprecision, uncertainty. It is used in artificial intelligence to implement inexact computational solutions, for which an exact solution cannot be derived in polynomial time. We will use this approach to segment fuzzy data. O

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