Results 41 to 50 of about 2,020,211 (266)
New bounds for $k$-means and information $k$-means
, 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
Fast k-means algorithm clustering
, 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
Clustering with Spectral Norm and the k-means Algorithm [PDF]
, 2010 There has been much progress on efficient algorithms for clustering data points generated by a mixture of $k$ probability distributions under the assumption that the means of the distributions are well-separated, i.e., the distance between the means of ...
Kannan, Ravindran, Kumar, Amit
core +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Blinatumomab has been shown to be highly effective for patients with pediatric B‐ALL and has recently become standard of care therapy. Due to its past use in the clinical trial setting, there is limited information available about real‐world administration.
Katelyn Oranges +12 more
wiley +1 more source
Efficient High-Dimensional Kernel k-Means++ with Random Projection
Applied 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
PCA and K-Means decipher genome
, 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
Pediatric Blood &Cancer, EarlyView.ABSTRACT Purpose Retinoblastoma (RB) is the most common pediatric ocular cancer, yet population‐based data on survival and risk factors remain limited. This study aimed to describe survival in a large national RB cohort and identify predictors of death and complications.
Samuel Sassine +14 more
wiley +1 more source
, 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
Scalable k-Means Clustering via Lightweight Coresets
, 2018 Coresets are compact representations of data sets such that models trained on a coreset are provably competitive with models trained on the full data set. As such, they have been successfully used to scale up clustering models to massive data sets. While
Arthur David +4 more
core +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background and Aims Wilms tumour (WT) has excellent event‐free and overall survival (OS). However, small differences exist between countries participating in the same international study. This led us to examine variation in adherence to protocol recommendations as a potential contributing factor.
Suzanne Tugnait +23 more
wiley +1 more source