Results 71 to 80 of about 18,390,026 (306)

Utility of Computed Tomography Surveillance of Asymptomatic Infection in Children and Young Adults Before Allogeneic Hematopoietic Stem Cell Transplant

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Asymptomatic infection poses a significant risk for children undergoing hematopoietic stem cell transplantation (HSCT). Pre‐transplant surveillance computed tomography (CT) is commonly used to identify occult infection, though its diagnostic yield remains uncertain.
Tyler Obermark, Pradipta Debnath, Rama S. Ayyala, Lara A. Danziger‐Isakov, Jane Koo, Kasiani C. Myers, William R. Otto, Aki Tanimoto, Andrew T. Trout, Cara E. Morin   +9 more
wiley   +1 more source

Deep k-Means: Jointly clustering with k-Means and learning representations

Pattern Recognition Letters, 2020
Under consideration at Pattern Recognition ...
Moradi Fard, Maziar, Thonet, Thibaut, Gaussier, Éric   +2 more
openaire   +4 more sources

Application of Machine Learning-Based K-means Clustering for Financial Fraud Detection

Academic Journal of Science and Technology
In today's increasingly digital financial landscape, the frequency and complexity of fraudulent activities are on the rise, posing significant risks and losses for both financial institutions and consumers.
Zengyi Huang, Haotian Zheng, Chen Li, Chang Che   +3 more
semanticscholar   +1 more source

Multiple Kernel Clustering with Kernel k-Means Coupled Graph Tensor Learning

AAAI Conference on Artificial Intelligence, 2021
Kernel k-means (KKM) and spectral clustering (SC) are two basic methods used for multiple kernel clustering (MKC), which have both been widely used to identify clusters that are non-linearly separable. However, both of them have their own shortcomings: 1)
Zhenwen Ren, Quansen Sun, Dong Wei
semanticscholar   +1 more source

Unheard and Under‐Supported: Health‐Related Quality of Life in Children, Adolescents, and Young Adults With Sickle Cell Disease

Pediatric Blood &Cancer, EarlyView.
Abstract Background Sickle cell disease (SCD) is an autosomal recessive hemoglobinopathy affecting millions of individuals worldwide. The clinical expression and psychosocial burden of SCD vary widely across geographical, cultural, and healthcare system contexts, underscoring the need for setting‐specific approaches to assessment.
Desiré Fantasia, Giulia Reggiani, Elizabeth J. Maran, Maria Elisa Delle Fave, Ilaria Baido, Anna Chiara Frigo, Maria Paola Boaro, Raffaella Colombatti   +7 more
wiley   +1 more source

CLUSTERING ANALYSIS FOR GROUPING SUB-DISTRICTS IN BOJONEGORO DISTRICT WITH THE K-MEANS METHOD WITH A VARIETY OF APPROACHES

Barekeng
Population data is an important piece of information that is useful for regional planning and development. Insight into the state of an area is more straightforward to observe if there are grouped sub-districts.
Denny Nurdiansyah, Mochamad Nizar Palefi Ma'ady, Yuana Sukmawaty, Muchammad Chandra Cahyo Utomo, Tia Mutiani   +4 more
doaj   +1 more source

Kernel Probabilistic K-Means Clustering

Sensors, 2021
Kernel fuzzy c-means (KFCM) is a significantly improved version of fuzzy c-means (FCM) for processing linearly inseparable datasets. However, for fuzzification parameter m=1, the problem of KFCM (kernel fuzzy c-means) cannot be solved by Lagrangian ...
Bowen Liu, Ting Zhang, Yujian Li, Zhaoying Liu, Zhilin Zhang   +4 more
doaj   +1 more source

A parametric k-means algorithm [PDF]

Computational Statistics, 2007
The k points that optimally represent a distribution (usually in terms of a squared error loss) are called the k principal points. This paper presents a computationally intensive method that automatically determines the principal points of a parametric distribution.
openaire   +3 more sources

Fast k-means based on KNN Graph

, 2017
In the era of big data, k-means clustering has been widely adopted as a basic processing tool in various contexts. However, its computational cost could be prohibitively high as the data size and the cluster number are large.
Deng, Cheng-Hao, Zhao, Wan-Lei
core   +1 more source

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

Scientific Reports
This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering.
Manoharan Premkumar, Garima Sinha, Manjula Devi Ramasamy, Santhoshini Sahu, Chithirala Bala Subramanyam, R. Sowmya, L. Abualigah, Bizuwork Derebew   +7 more
semanticscholar   +1 more source