Results 281 to 290 of about 66,224 (345)

Suppressed fuzzy c-means clustering algorithm

Pattern Recognition Letters, 2003
Summary: Based on the defect of rival checked fuzzy \(c\)-means clustering algorithm, a new algorithm: suppressed fuzzy \(c\)-means clustering algorithm is proposed. The new algorithm overcomes the shortcomings of the original algorithm, establishes more natural and more reasonable relationships between hard \(c\)-means clustering algorithm and fuzzy \(
Fan, Jiu-Lun, Zhen, Wen-Zhi, Xie, Wei-Xin   +2 more
openaire   +2 more sources

General Fuzzy C-Means Clustering Strategy: Using Objective Function to Control Fuzziness of Clustering Results

IEEE transactions on fuzzy systems, 2022
As one of the most commonly used clustering methods, the fuzzy C-means (FCM) clustering strategy extends the notion of hard clustering to associate each pattern with every cluster using a membership function.
Kaixin Zhao, Yaping Dai, Zhiyang Jia, Ye Ji   +3 more
semanticscholar   +1 more source

Fuzzy c-means clustering of incomplete data

IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2001
The problem of clustering a real s-dimensional data set X={x(1 ),,,,,x(n)} subset R(s) is considered. Usually, each observation (or datum) consists of numerical values for all s features (such as height, length, etc.), but sometimes data sets can contain vectors that are missing one or more of the feature values.
R J, Hathaway, J C, Bezdek
openaire   +2 more sources

Dynamic committee machine with fuzzy-c-means clustering for total organic carbon content prediction from wireline logs

Computational Geosciences, 2021
The total organic carbon (TOC) content is of great significance to reflect the hydrocarbon-generation potential in shale reservoirs. The well logs were always used to predict the TOC content, but some linear regression methods do not match well with ...
Yang Bai, M. Tan
semanticscholar   +1 more source

Pythagorean Fuzzy c-means Clustering Algorithm

2021
This article presents algorithm for \(c\)-means clustering under Pythagorean fuzzy environment. In this method Pythagorean fuzzy generator is developed to convert the data points from crisp to Pythagorean fuzzy numbers (PFNs). Subsequently, Euclidean distance is used to measure the distances between data points.
Souvik Gayen, Animesh Biswas
openaire   +1 more source

Robust fuzzy c-means clustering algorithm with adaptive spatial & intensity constraint and membership linking for noise image segmentation

Applied Soft Computing, 2020
The fuzzy C-means (FCM) clustering method is proven to be an efficient method to segment images. However, the FCM method is not robustness and less accurate for noise images.
Qingsheng Wang, Xiaopeng Wang, Chaoqun Fang, Wenting Yang   +3 more
semanticscholar   +1 more source

Robust weighted fuzzy c-means clustering

2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence), 2008
Nowadays, the fuzzy c-means method (FCM) became 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. This paper presents a robust clustering algorithm called robust weighted fuzzy c-means (RWFCM).
A. H. Hadjahmadi, M. M. Homayounpour, S. M. Ahadi   +2 more
openaire   +1 more source

Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering

Int. J. Medical Informatics, 2019
OBJECTIVE Melanoma is a dangerous form of the skin cancer responsible for thousands of deaths every year. Early detection of melanoma is possible through visual inspection of pigmented lesions over the skin, treated with simple excision of the cancerous ...
Nudrat Nida, Aun Irtaza, A. Javed, M. Yousaf, M. Mahmood   +4 more
semanticscholar   +1 more source

An Efficient Federated Multiview Fuzzy C-Means Clustering Method

IEEE transactions on fuzzy systems
Multiview clustering has been received considerable attention due to the widespread collection of multiview data from diverse domains and sources. However, storing multiview data across multiple devices in many real scenarios poses significant challenges
Xingchen Hu, Jindong Qin, Yinghua Shen, W. Pedrycz, Xinwang Liu, Jiyuan Liu   +5 more
semanticscholar   +1 more source

Projected fuzzy C-means clustering with locality preservation

Pattern Recognition, 2020
Traditional partition-based clustering algorithms, hard or fuzzy version of C-means, could not deal with high-dimensional data sets effectively as redundant features may impact the computation of distances and local spatial structures among patterns are ...
Jie Zhou, W. Pedrycz, Xiaodong Yue, C. Gao, Zhihui Lai, Jun Wan   +5 more
semanticscholar   +1 more source