Results 41 to 50 of about 2,006,480 (313)

Dynamic load balancing in parallel KD-tree k-means [PDF]

open access: yes, 2010
One among the most influential and popular data mining methods is the k-Means algorithm for cluster analysis. Techniques for improving the efficiency of k-Means have been largely explored in two main directions.
Di Fatta, Giuseppe, Pettinger, David
core   +1 more source

RSKC: An R Package for a Robust and Sparse K-Means Clustering Algorithm

open access: yesJournal of Statistical Software, 2016
Witten and Tibshirani (2010) proposed an algorithim to simultaneously find clusters and select clustering variables, called sparse K-means (SK-means).
Yumi Kondo   +2 more
doaj   +1 more source

A Novel Active Noise Control Method Based on Variational Mode Decomposition and Gradient Boosting Decision Tree

open access: yesApplied Sciences, 2023
Diversified noise sources pose great challenges in the engineering of an ANC (active noise control) system design. To solve this problem, this paper proposes an ANC method based on VMD (variational mode decomposition) and Ensemble Learning.
Xiaobei Liang   +4 more
doaj   +1 more source

Fast k-means algorithm clustering

open access: yes, 2011
k-means has recently been recognized as one of the best algorithms for clustering unsupervised data. Since k-means depends mainly on distance calculation between all data points and the centers, the time cost will be high when the size of the dataset is ...
Kecman, Vojislav   +4 more
core   +1 more source

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

PCA and K-Means decipher genome

open access: yes, 2008
In this paper, we aim to give a tutorial for undergraduate students studying statistical methods and/or bioinformatics. The students will learn how data visualization can help in genomic sequence analysis.
A Zinovyev   +8 more
core   +2 more sources

A Quality Improvement Initiative to Standardize Pneumocystis jirovecii Pneumonia Prophylaxis in Pediatric Patients With Solid Tumors

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Background Pediatric patients with extracranial solid tumors (ST) receiving chemotherapy are at an increased risk for Pneumocystis jirovecii pneumonia (PJP). However, evidence guiding prophylaxis practices in this population is limited. A PJP‐related fatality at our institution highlighted inconsistent prescribing approaches and concerns about
Kriti Kumar   +8 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

Sickle Cell Disease Is an Inherent Risk for Asthma in a Sibling Comparison Study

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Introduction Sickle cell disease (SCD) and asthma share a complex relationship. Although estimates vary, asthma prevalence in children with SCD is believed to be comparable to or higher than the general population. Determining whether SCD confers an increased risk for asthma remains challenging due to overlapping symptoms and the ...
Suhei C. Zuleta De Bernardis   +9 more
wiley   +1 more source

Online k-means Clustering

open access: yes, 2019
We study the problem of online clustering where a clustering algorithm has to assign a new point that arrives to one of $k$ clusters. The specific formulation we use is the $k$-means objective: At each time step the algorithm has to maintain a set of k candidate centers and the loss incurred is the squared distance between the new point and the closest
Cohen-Addad, Vincent   +3 more
openaire   +3 more sources

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