Results 111 to 120 of about 112,252 (298)
Clusters of medical specialties around patients with multimorbidity - employing fuzzy c-means clustering to explore multidisciplinary collaboration. [PDF]
BMC Health Serv Res, 2023 Verhoeff M +8 more
europepmc +1 more source
Extended Fuzzy Clustering Algorithms [PDF]
Fuzzy clustering is a widely applied method for obtaining fuzzy models from data. Ithas been applied successfully in various fields including finance and marketing.
Kaymak, U., Setnes, M.
core +1 more source
A Comparison of Fuzzy Clustering Algorithms Applied to Feature Extraction on Vineyard [PDF]
, 2011 Image segmentation is a process by which an image is partitioned into regions with similar features. Many approaches have been proposed for color image segmentation, but Fuzzy C-Means has been widely used, because it has a good performance in a large ...
Barreiro Elorza, Pilar +4 more
core
Scalable clustering by truncated fuzzy $c$-means
Big Data and Information Analytics, 2016 Most existing clustering algorithms are slow for dividing a large dataset into a large number of clusters. In this paper, we propose a truncated FCM algorithm to address this problem. The main idea behind our proposed algorithm is to keep only a small number of cluster centers during the iterative process of the FCM algorithm. Our numerical experiments
Guojun Gan, Qiujun Lan, Shiyang Sima
openaire +1 more source
Unpaired Learning‐Enabled Nanotube Identification from AFM Images
Advanced Science, EarlyView.Identifying nanotubes on rough substrates is notoriously challenging for conventional image analysis. This work presents an unpaired deep learning approach that automatically extracts nanotube networks from atomic force microscopy images, even on complex polymeric surfaces used in roll‐to‐roll printing.
Soyoung Na +10 more
wiley +1 more source
Self-organization and clustering algorithms [PDF]
Kohonen's feature maps approach to clustering is often likened to the k or c-means clustering algorithms. Here, the author identifies some similarities and differences between the hard and fuzzy c-Means (HCM/FCM) or ISODATA algorithms and Kohonen's self ...
Bezdek, James C.
core +1 more source
Atomistic Understanding of 2D Monatomic Phase‐Change Material for Non‐Volatile Optical Applications
Advanced Science, EarlyView.Antimony is a promising monatomic phase‐change material. Scaling down the film thickness is necessary to prolong the amorphous‐state lifetime, but it alters the optical properties. The combined computational and experimental study shows that, as thickness decreases, the extinction coefficient and optical contrast are reduced in the near‐infrared ...
Hanyi Zhang +10 more
wiley +1 more source
Incremental Beta Distribution Weighted Fuzzy C-Ordered Means Clustering
InformationStreaming data is becoming more and more common in the field of big data and incremental frameworks can address its complexity. The BDFCOM algorithm achieves good results on common form datasets by introducing the ordering mechanism of beta distribution ...
Hengda Wang +3 more
doaj +1 more source
Performance evaluation of spatial fuzzy C-means clustering algorithm on GPU for image segmentation. [PDF]
Multimed Tools Appl, 2023 Ali NA, El Abbassi A, Bouattane O.
europepmc +1 more source
Advanced Science, EarlyView.The prevailing neglect of cellular hierarchies in current spatial transcriptomics deconvolution often obscures cellular heterogeneity and impedes the identification of fine‐grained subtypes. To address this issue, HIDF employs a cluster‐tree and dual regularization to systematically model cellular hierarchical structures.
Zhiyi Zou +5 more
wiley +1 more source