Results 31 to 40 of about 112,252 (298)
Fuzzy Clustering Using C-Means Method
TEM Journal, 2015 The cluster analysis of fuzzy clustering according to the fuzzy c-means algorithm has been described in this paper: the problem about the fuzzy clustering has been discussed and the general formal concept of the problem of the fuzzy clustering analysis has been presented.
Georgi Krastev, Tsvetozar Georgiev
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Fuzzy C-means method for clustering microarray data [PDF]
Bioinformatics, 2003 Abstract Motivation: Clustering analysis of data from DNA microarray hybridization studies is essential for identifying biologically relevant groups of genes. Partitional clustering methods such as K-means or self-organizing maps assign each gene to a single cluster.
Dembélé, Doulaye, Kastner, Philippe
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IMPLEMENTASI FUZZY C-MEANS CLUSTERING DALAM PENENTUAN BEASISWA
Barekeng, 2017 Logika fuzzy merupakan salah satu komponen pembentuk Soft Computing, yaitu suatu cara yang tepat untuk memetakan suatu ruang input ke dalam suatu ruang output.
Dorteus L. Rahakbauw +2 more
doaj +1 more source
Clustering Analysis for the Pareto Optimal Front in Multi-Objective Optimization
Computation, 2022 Bio-inspired algorithms are a suitable alternative for solving multi-objective optimization problems. Among different proposals, a widely used approach is based on the Pareto front.
Lilian Astrid Bejarano +2 more
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Penerapan Fuzzy C-Means Clustering untuk Mengoptimalkan Penentuan Media Promosi [PDF]
, 2016 Media promosi merupakan salah satu faktor penting dalam pemasaran suatu perguruan tinggi untuk menarik minat calon mahasiswa agar melanjutkan pendidikannya ke tingkat yang lebih tinggi. Pemerataan pendistribusian media promosi pada STIKOM Binaniaga yang
Sarjanako, R. J. (R)
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FEBS Open Bio, EarlyView.Mouse pre‐implantation development involves a transition from totipotency to pluripotency. Integrating transcriptomics, epigenetic profiling, low‐input proteomics and functional assays, we show that eight‐cell embryos retain residual totipotency features, whereas cytoskeletal remodeling regulated by the ubiquitin‐proteasome system drives progression ...
Wanqiong Li +8 more
wiley +1 more source
Electrical fuzzy C-means: A new heuristic fuzzy clustering algorithm
Cogent Engineering, 2016 Many heuristic and meta-heuristic algorithms have been successfully applied in the literature to solve the clustering problems. The algorithms have been created for partitioning and classifying a set of data because of two main purposes: at first, for ...
Esmaeil Mehdizadeh, Amir Golabzaei
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Fuzzy Image Segmentation using Suppressed Fuzzy C-Means Clustering (SFCM) [PDF]
, 2004 Clustering algorithms are highly dependent on the features used and the type of the objects in a particular image. By considering object similar surface variations (SSV) as well as the arbitrariness of the fuzzy c-means (FCM) algorithm for pixel location,
Ali, Ameer +2 more
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Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To investigate the value of constructing models based on habitat radiomics and pathomics for predicting the risk of progression in high‐grade gliomas. Methods This study conducted a retrospective analysis of preoperative magnetic resonance (MR) images and pathological sections from 72 patients diagnosed with high‐grade gliomas (52 ...
Yuchen Zhu +14 more
wiley +1 more source
Performance characterization of clustering algorithms for colour image segmentation [PDF]
, 2006 This paper details the implementation of three traditional clustering techniques (K-Means clustering, Fuzzy C-Means clustering and Adaptive K-Means clustering) that are applied to extract the colour information that is used in the image segmentation ...
Ghita, Ovidiu +2 more
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