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Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms [PDF]
Advances in Fuzzy Systems, 2015 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)
Arindam Chaudhuri
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Weighted-covariance factor fuzzy C-means clustering [PDF]
2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2015 In this paper, we propose a factor weighted fuzzy c-means clustering algorithm. Based on the inverse of a covariance factor, which assesses the collinearity between the centers and samples, this factor takes also into account the compactness of the ...
Bertrand, Isabelle +4 more
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Differential privacy fuzzy C-means clustering algorithm based on gaussian kernel function. [PDF]
PLoS ONE, 2021 Fuzzy C-means clustering algorithm is one of the typical clustering algorithms in data mining applications. However, due to the sensitive information in the dataset, there is a risk of user privacy being leaked during the clustering process.
Yaling Zhang, Jin Han
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Conditional semi‐fuzzy c‐means clustering for imbalanced dataset
IET Image Processing, 2020 Fuzzy c‐means algorithms have been widely utilised in several areas such as image segmentation, pattern recognition and data mining. However, the related studies showed the limitations in facing imbalanced datasets. The maximum fuzzy boundary tends to be
Yunlong Gao +4 more
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Bilateral Weighted Fuzzy C-Means Clustering
Iranian Journal of Electrical and Electronic Engineering, 2012 Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise.
A. H. Hadjahmadi +2 more
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An improved fuzzy clustering image segmentation algorithm combining spatial information
Xi'an Gongcheng Daxue xuebao, 2021 In order to improve the ability of fuzzy C-means (FCM) clustering algorithm to suppress noise, an improved fuzzy clustering image segmentation algorithm was proposed.
Xudong LIU +4 more
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K-means and fuzzy c-means algorithm comparison on regency/city grouping in Central Java Province
Desimal, 2022 The Human Development Index (HDI) is very important in measuring the country's success as an effort to build the quality of life of people in a region, including Indonesia. The government needs to make groupings based on the needs of a city/district.
Ummu Wachidatul Latifah +2 more
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A Federated Fuzzy c-means Clustering Algorithm
Proceedings of WILF 2021, the 13th International Workshop on Fuzzy Logic and Applications (WILF 2021), 2021 Traditional clustering algorithms require data to be centralized on a single machine or in a datacenter. Due to privacy issues and traffic limitations, in several real applications data cannot be transferred, thus hampering the effectiveness of traditional clustering algorithms, which can operate only on locally stored data.
B��rcena, Jos�� Luis Corcuera +4 more
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Intuitionistic Fuzzy c-Ordered Means Clustering Algorithm
IEEE Access, 2022 Atanassov intuitionistic fuzzy set (AIFS) has the capability to deal with various uncertain situations, so its popularity among researchers is quite high.
Meenakshi Kaushal, Q. M. Danish Lohani
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Implementation of Fuzzy C-Means for Clustering the Majelis Ulama Indonesia (MUI) Fatwa Documents
JOIN: Jurnal Online Informatika, 2021 Since the Indonesian Ulema Council (MUI) was established in 1975 until now, this institution has produced 201 edicts covering various fields. Text mining is one of the techniques used to collect data hidden from data that form text.
Fajar Rohman Hariri
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