Results 71 to 80 of about 14,371,534 (281)

Early Body Mass Index z‐Score Change and Resolution of Severe Malnutrition in Children With Sickle Cell Anemia in a Low‐Income Setting: A Prospective Single‐Arm Extension Study

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Background Children with sickle cell anemia (SCA) in low‐income settings are at risk of severe malnutrition, but optimal nutritional management has not been established. We evaluated an intensified ready‐to‐use therapeutic food (RUTF) regimen in children with persistent severe malnutrition after initial treatment and assessed whether early ...
Safiya Gambo   +9 more
wiley   +1 more source

Efficient High-Dimensional Kernel k-Means++ with Random Projection

open access: yesApplied Sciences, 2021
Using random projection, a method to speed up both kernel k-means and centroid initialization with k-means++ is proposed. We approximate the kernel matrix and distances in a lower-dimensional space Rd before the kernel k-means clustering motivated by ...
Jan Y. K. Chan, Alex Po Leung, Yunbo Xie
doaj   +1 more source

Inference with K-means

open access: yesCoRR
15 ...
Alfred K. Adzika, Prudence Djagba
openaire   +2 more sources

Central Nervous System Tumors Among Infants in Canada: A Report From CYP‐C

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Background Central nervous system (CNS) tumors in infants are rare, pose unique clinical challenges, and lack large‐scale evidence‐based data to guide management. This study seeks to describe CNS tumors in Canadian infants and to compare their outcomes with those of older children.
Samuel Sassine   +17 more
wiley   +1 more source

Color image segmentation using a spatial k-means clustering algorithm [PDF]

open access: yes, 2006
This paper details the implementation of a new adaptive technique for color-texture segmentation that is a generalization of the standard K-Means algorithm. The standard K-Means algorithm produces accurate segmentation results only when applied to images
Ilea, Dana E., Whelan, Paul F.
core   +2 more sources

New bounds for $k$-means and information $k$-means

open access: yes, 2021
In this paper, we derive a new dimension-free non-asymptotic upper bound for the quadratic $k$-means excess risk related to the quantization of an i.i.d sample in a separable Hilbert space. We improve the bound of order $\mathcal{O} \bigl( k / \sqrt{n} \bigr)$ of Biau, Devroye and Lugosi, recovering the rate $\sqrt{k/n}$ that has already been proved by
Appert, Gautier, Catoni, Olivier
openaire   +2 more sources

Comparative Drug Response Profiling in Neuroblastoma Cell Lines and Patient‐Derived Tumor Organoids

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT High‐risk neuroblastoma remains a leading cause of pediatric cancer mortality, and improved preclinical models are needed to guide therapeutic developments. We screened seven high‐risk neuroblastoma cell lines and three patient‐derived tumor organoids with 528 compounds alongside bone marrow controls, and compared them with external datasets ...
Krzysztof Wierbiłowicz   +12 more
wiley   +1 more source

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

open access: yesBarekeng
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   +4 more
doaj   +1 more source

Soil data clustering by using K-means and fuzzy K-means algorithm

open access: yesTelfor Journal, 2016
A problem of soil clustering based on the chemical characteristics of soil, and proper visual representation of the obtained results, is analysed in the paper. To that aim, K-means and fuzzy K-means algorithms are adapted for soil data clustering.
E. Hot, V. Popović-Bugarin
doaj   +1 more source

Balanced K-Means for Clustering [PDF]

open access: yes, 2014
We present a k-means-based clustering algorithm, which optimizes mean square error, for given cluster sizes. A straightforward application is balanced clustering, where the sizes of each cluster are equal. In k-means assignment phase, the algorithm solves the assignment problem by Hungarian algorithm.
Malinen Mikko, Fränti Pasi
openaire   +2 more sources

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