Results 201 to 210 of about 15,044 (262)

A black-winged kite improved fuzzy clustering handling imbalanced uncertain data. [PDF]

PLoS One
Tran-Nam H, Che-Ngoc H.
europepmc   +1 more source

Fueling Tomorrow: Scenario Planning for the Future of Gas Stations

Business Strategy and the Environment, EarlyView.
ABSTRACT Transport electrification is reshaping the service infrastructures that mediate everyday mobility, yet most electrification scenario studies remain macrolevel and offer limited insight into how incumbent forecourt (gas‐station) networks can adapt under deep uncertainty.
Joao Gabriel Rosa, Fabiano Armellini, Jean‐Marc Robert   +2 more
wiley   +1 more source

Deconstructing Biological Sex by Fuzzifying Osteological Sex: Implications for Theoretically Informed Practice. [PDF]

Am J Biol Anthropol
Adams DM, Lane KM.
europepmc   +1 more source

Hybrid deep learning approach for early emphysema diagnosis combining fuzzy C-means, TransUNet, and faster mask R-CNN. [PDF]

Sci Rep
Dharmaraj M, Murugesan A.
europepmc   +1 more source

A hybrid fuzzy-ensemble method for time series forecasting. [PDF]

Sci Rep
Dalar AZ.
europepmc   +1 more source

Analysis and visualization of expression patterns with fuzzy sets as FlowSets. [PDF]

NAR Genom Bioinform
Offensperger F, Joppich M, Sinn E, Zimmer R.   +3 more
europepmc   +1 more source

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Relative entropy fuzzy c-means clustering

Information Sciences, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Marzieh Zarinbal, I B Turksen, M H Fazel Zarandi   +2 more
exaly   +5 more sources

A possibilistic fuzzy c-means clustering algorithm

IEEE Transactions on Fuzzy Systems, 2005
In 1997, we proposed the fuzzy-possibilistic c-means (FPCM) model and algorithm that generated both membership and typicality values when clustering unlabeled data. FPCM constrains the typicality values so that the sum over all data points of typicalities to a cluster is one.
J M Keller, J C Bezdek, N R Pal
exaly   +3 more sources

Generalized Fuzzy C-Means Clustering Algorithm With Improved Fuzzy Partitions

IEEE Transactions on Systems, Man, and Cybernetics, 2009
The fuzziness index m has important influence on the clustering result of fuzzy clustering algorithms, and it should not be forced to fix at the usual value m = 2. In view of its distinctive features in applications and its limitation in having m = 2 only, a recent advance of fuzzy clustering called fuzzy c-means clustering with improved fuzzy ...
Fu Lai Korris Chung, Shitong Wang
exaly   +3 more sources

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