Results 51 to 60 of about 803,970 (265)
Clustering for point pattern data [PDF]
2016 23rd International Conference on Pattern Recognition (ICPR), 2016 Preprint: 23rd Int. Conf. Pattern Recognition (ICPR).
Nhat-Quang Tran +3 more
openaire +2 more sources
Pediatric Blood &Cancer, EarlyView.ABSTRACT A lethal round‐cell malignancy with an MN1::ZNF341 fusion has recently been reported in three infants. Here, we describe four further tumors, three in newborns (including monozygotic twins), and one in an adolescent. Detailed clinical, radiological, and histopathological data differentiate these tumors from their main mimics, neuroblastoma and
Thomas R. W. Oliver +25 more
wiley +1 more source
GPU Hızlandırmalı Veri Demetleme Algoritmalarının İncelenmesi
Online Academic Journal of Information Technology, 2013 Veri demetleme algoritmaları, arama; spam, saldırı tespiti; hücre, gen, doküman analizi; moleküler dinamik simülasyonlarının biçimlerinin analizi gibi uygulamalar için oldukça önemlidirler.
Murat Hacıömeroğlu +1 more
doaj +1 more source
Sensors, 2022 Clustering using fuzzy C-means (FCM) is a soft segmentation method that has been extensively investigated and successfully implemented in image segmentation. FCM is useful in various aspects, such as the segmentation of grayscale images. However, FCM has
Waleed Alomoush +6 more
doaj +1 more source
Clustering with Distributed Data
CoRR, 2019 We consider $K$-means clustering in networked environments (e.g., internet of things (IoT) and sensor networks) where data is inherently distributed across nodes and processing power at each node may be limited. We consider a clustering algorithm referred to as networked $K$-means, or $NK$-means, which relies only on local neighborhood information ...
Soummya Kar, Brian Swenson
openaire +2 more sources
High-dimensional data clustering [PDF]
Computational Statistics & Data Analysis, 2007 Clustering in high-dimensional spaces is a difficult problem which is recurrent in many domains, for example in image analysis. The difficulty is due to the fact that high-dimensional data usually live in different low-dimensional subspaces hidden in the original space.
Bouveyron, Charles +2 more
openaire +6 more sources
Pediatric Blood &Cancer, EarlyView.ABSTRACT We report a retrospective single‐center analysis of pediatric patients with relapsed or refractory B‐cell precursor acute lymphoblastic leukemia focusing on relapses outside of the typical locations, bone marrow, central nervous system, or testes.
Johanna Kunz +7 more
wiley +1 more source
A Bibliometric Analysis of Publications in Uremic Toxins From 1991 to 2024
Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Uremic toxins are a growing area of research in nephrology, with significant implications in the progression and treatment of chronic kidney disease (CKD) and the management of end‐stage kidney disease (ESKD). This bibliometric analysis aims to evaluate the global research trends, key contributors, and the impact of publications in ...
Yuh‐Shan Ho +7 more
wiley +1 more source
A Hybrid Data Clustering Algorithm Using Modified Krill Herd Algorithm and K-MEANS [PDF]
Journal of Advances in Computer Engineering and Technology, 2019 Data clustering is the process of partitioning a set of data objects into meaning clusters or groups. Due to the vast usage of clustering algorithms in many fields, a lot of research is still going on to find the best and efficient clustering algorithm ...
Jensi R
doaj
Data ultrametricity and clusterability
Journal of Physics: Conference Series, 2019 Abstract The increasing needs of clustering massive datasets and the high cost of running clustering algorithms poses difficult problems for users. In this context it is important to determine if a data set is clusterable, that is, it may be partitioned efficiently into well-differentiated groups containing similar objects.
Dan A. Simovici, Kaixun Hua
openaire +2 more sources