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Relevant bibliographies by topics / Fuzzy Possibilistic C-Means

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Academic literature on the topic 'Fuzzy Possibilistic C-Means'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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

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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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Dissertations / Theses on the topic "Fuzzy Possibilistic C-Means"

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Amornnikun, Patipharn, and Patipharn Amornnikun. "Metaheuristic-Based Possibilistic Multivariate Fuzzy Weighted C-Means Algorithms for Market Segmentation." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/swmwcm.

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Hsieh, Chih-Chung, and 謝執中. "Texture feature analysis with fuzzy possibilistic c-means for brain MR image segmentation." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/54057332299638956757.

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碩士<br>國立臺灣大學<br>工程科學及海洋工程學研究所<br>103<br>Segmentation of brain tissue from non-brain tissue, also known as skull stripping, has been challenging due to the complexity of human brain structures and variable parameters of MR scanners. It is one of the most important preprocessing steps in medical image analysis. Skull stripping is often performed using a sequence of mathematical morphological operations following an initial separation of the brain from other tissues of the head. We propose a new brain segmentation algorithm that is based on a texture feature analysis, fuzzy possibilistic c-means

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Chang, Sheng-Chieh, and 張勝傑. "Rough Interval Possibilistic Fuzzy C-Means Clustering Algorithms and Implemented on Smart Phone." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/e57f8z.

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碩士<br>國立虎尾科技大學<br>光電與材料科技研究所<br>100<br>Clustering algorithms have been widely used such as pattern recognition, data mining and machine learning, etc. It is an unsupervised classification that is divided into groups according to data sets. That is, the data sets of similarity partition belong to the same group; otherwise data sets divide other groups in the clustering algorithms. In general, clustering methods are divided into partitioning-based, hierarchical, density-based, grid-based and model-based. In this thesis, we focus on the partitioning-based approach. K-means (KM) clustering algorit

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Hsu, Chia-Cheng, and 許家誠. "Hybrid of Multi-objective Meta-heuristics and Possibilistic Intuitionistic Fuzzy c-means Algorithms for Cluster Analysis." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/d4rycx.

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碩士<br>國立臺灣科技大學<br>工業管理系<br>106<br>Due to the rapid growth of information, complicated data and information can be collected more easily. Therefore, how to reveal important information from the data becomes a very important issue. Clustering analysis is an important technique in data mining. However, there is no clustering method which can correctly cluster all different datasets. Thus, this study proposes hybrid of multi-objective meta-heuristics and possibilistic intuitionistic fuzzy c-means (PIFCM) algorithm. In order to provide a better clustering result, this study considers multi-objectiv

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LIN, JUN-YU, and 林峻宇. "An Application of Sine Cosine Algorithm-based Fuzzy Possibilistic c-ordered Means Algorithm to Customer Segmentation." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/j7h44r.

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碩士<br>國立臺灣科技大學<br>工業管理系<br>107<br>Due to advances in information technology, data collection is becoming much easier. Clustering is an important technique for exploring data structures and is used in many fields, such as customer segmentation, image recognition, social science and so on. However, in real–world applications, there are a lot of noises or outliers which will influence the clustering performance in the dataset. Besides, the clustering results are susceptible to the initial centroids and algorithm parameters. Therefore, in order to overcome the influence of outliers on clustering r

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Book chapters on the topic "Fuzzy Possibilistic C-Means"

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Himmelspach, Ludmila, and Stefan Conrad. "A Possibilistic Multivariate Fuzzy c-Means Clustering Algorithm." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45856-4_24.

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Rubio, E., Oscar Castillo, and Patricia Melin. "Interval Type-2 Fuzzy Possibilistic C-Means Clustering Algorithm." In Recent Developments and New Direction in Soft-Computing Foundations and Applications. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32229-2_14.

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Tang, Yiming, Lei Xi, Wenbin Wu, Xi Wu, Shujie Li, and Rui Chen. "A Weighting Possibilistic Fuzzy C-Means Algorithm for Interval Granularity." In Computer Supported Cooperative Work and Social Computing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2385-4_26.

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Subudhi, Sharmila, and Suvasini Panigrahi. "Use of Possibilistic Fuzzy C-means Clustering for Telecom Fraud Detection." In Advances in Intelligent Systems and Computing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3874-7_60.

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Tang, Yiming, Zhifu Pan, Hongmang Li, and Lei Xi. "Kernel Subspace Possibilistic Fuzzy C-Means Algorithm Driven by Feature Weights." In Computer Supported Cooperative Work and Social Computing. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4546-5_23.

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Vijaya, J., and Hussian Syed. "A Performance Study of Probabilistic Possibilistic Fuzzy C-Means Clustering Algorithm." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81462-5_39.

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Wendling, L., and J. Desachy. "Pattern recognition of strong graphs based on possibilistic c-means and k-formulae matching." In Fuzzy Logic in Artificial Intelligence. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/bfb0095078.

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Rustam, Zuherman, Sri Hartini, Titin Siswantining, Dea Aulia Utami, and Nadisa Karina Putri. "Comparison Between Fuzzy Kernel C-Means, Fuzzy Kernel Possibilistic C-Means and Support Vector Machines in Soft Tissue Tumor Classification." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36664-3_11.

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Paul, Anal, and Santi P. Maity. "On Energy Efficient Cooperative Spectrum Sensing Using Possibilistic Fuzzy C-Means Clustering." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6427-2_31.

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Saad, Mohamed Fadhel, and Mohamed Adel Alimi. "A New Improved Fuzzy Possibilistic C-Means Algorithm Based on Weight Degree." In Lecture Notes in Electrical Engineering. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-3517-2_7.

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Conference papers on the topic "Fuzzy Possibilistic C-Means"

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Naghi, Mirtill-Boglárka, Levente Kovács, and László Szilágyi. "A Parameter Selection Strategy for the Generalized Fuzzy-Possibilistic C-Means Algorithm." In 2025 IEEE 23rd World Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2025. https://doi.org/10.1109/sami63904.2025.10883325.

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Naghil, Mirtill-Bogláirka, Vladik Kreinovich, Levente Kovács, and László Szilágyi. "A Self-Tuning Version for the Fuzzy-Possibilistic Product Partition c-Means Algorithm." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831046.

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Maulana, Alvian Rizal, Masithoh Yessi Rochayani, and Moch Abdul Mukid. "Regional Clustering for Stunting Prevalence Analysis in Central Java Using Possibilistic Fuzzy C-Means (PFCM) Algorithm." In 2025 International Conference on Computer Sciences, Engineering, and Technology Innovation (ICoCSETI). IEEE, 2025. https://doi.org/10.1109/icocseti63724.2025.11019156.

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Chellappan, Dinesh, and Harikumar Rajaguru. "Swarm Intelligence Feature Selection and Possibilistic Fuzzy c-Means Approach for Enhancement of Classifier Performance in Detection of Diabetes from Microarray gene." In TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON). IEEE, 2024. https://doi.org/10.1109/tencon61640.2024.10902807.

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Ramathilagam, S., S. R. Kannan, and R. Devi. "Effective Fuzzy Possibilistic C-Means." In ASE BD&SI '15: ASE BigData & SocialInformatics 2015. ACM, 2015. http://dx.doi.org/10.1145/2818869.2818870.

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dos Santos, Matheus Magalhaes Batista, and Mauricio Pamplona Segundo. "Continuous biometric authentication using Possibilistic C-Means." In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2018. http://dx.doi.org/10.1109/fuzz-ieee.2018.8491508.

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Yong, Zhou, Li Yue'e, and Xia Shixiong. "Robust Fuzzy-Possibilistic C-Means Algorithm." In 2008 Second International Symposium on Intelligent Information Technology Application (IITA). IEEE, 2008. http://dx.doi.org/10.1109/iita.2008.146.

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Namkoong, Younghwan, Gyeongyong Heo, and Young Woon Woo. "An extension of possibilistic fuzzy c-means with regularization." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584538.

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Zarandi, M. H. Fazel, M. Rostam Niakan Kalhori, and M. F. Jahromi. "Possibilistic c-means clustering using fuzzy relations." In 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS). IEEE, 2013. http://dx.doi.org/10.1109/ifsa-nafips.2013.6608560.

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Sledge, Isaac, James Bezdek, Timothy Havens, and James Keller. "A relational dual of the fuzzy possibilistic c-means algorithm." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584846.

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