Results 51 to 60 of about 13,094,494 (259)

K-MEANS WITH SAMPLING FOR DETERMINING PROMINENT COLORS IN IMAGES

ICTACT Journal on Soft Computing, 2022
A tool that quickly calculates the dominant colors of an image can be very useful in image processing. The k-means clustering algorithm has this potential since it partitions a set of data into n clusters and returns a representative data point from each
Angelina Cheng, Eric Rosenberg, Alina Gorbunova   +2 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   +4 more sources

kingshukkundu/K-Means 1.0

, 2019
Implementation of K means in ...
Kingshuk Kundu
core   +1 more source

Scalability of efficient parallel K-Means [PDF]

, 2009
Clustering is defined as the grouping of similar items in a set, and is an important process within the field of data mining. As the amount of data for various applications continues to increase, in terms of its size and dimensionality, it is necessary ...
Giuseppe Di Fatta, Pettinger, David, Di Fatta, Giuseppe, David Pettinger   +3 more
core   +1 more source

A Feature-Reduction Multi-View k-Means Clustering Algorithm

IEEE Access, 2019
The k-means clustering algorithm is the oldest and most known method in cluster analysis. It has been widely studied with various extensions and applied in a variety of substantive areas.
Miin-Shen Yang, Kristina P. Sinaga
doaj   +1 more source

Evidence‐Informed Multidisciplinary Consensus Guidance for the Psychosocial Care of Adolescents With High‐Risk Cancer: Recommendations From the Italian Association of Pediatric Hematology and Oncology

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Adolescents with high‐risk cancer face complex developmental, psychosocial, and ethical challenges that extend beyond disease‐directed treatment. Although international recommendations exist for communication, psychosocial care, pediatric palliative care, survivorship, and shared decision‐making, these have largely evolved within ...
Johanna M. C. Blom, Dorella Scarponi, Giulia Zucchetti, Teresa Perillo, Maria Montanaro, Elisa Marconi, Chiara Rutigliano, Paolo Colavero, Paola Quarello, Marina Bertolotti, Monica Cellini, Carlo Clerici, Maurizio Mascarin, Francesca Bomben, Andrea Ferrari, the expert workgroup   +15 more
wiley   +1 more source

K+ Means : An Enhancement Over K-Means Clustering Algorithm

CoRR, 2017
Authors: Co-author's name added Section 3: Step (a) and (b) of K+Means algorithm are merged for simplicity. Section 3.1: K+ Means algorithm complexity rectified.
Srikanta Kolay, Kumar Sankar Ray, Abhoy Chand Mondal   +2 more
openaire   +2 more sources

K-means : genes of 4 clusters

, 2022
K-means : genes of 4 clusters 
Jee (13018611)
core   +1 more source

Bridging the Loneliness Gap: Depression, Connectivity, and Isolation in Pediatric Oncology Patients and Their Peers

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Loneliness is associated with adverse physical and mental health outcomes and remains understudied in children and adolescents undergoing cancer therapy. Pediatric oncology patients may be at increased risk due to medical isolation and disruption of social networks.
Charlotte N. Stahlfeld, Scott H. Maurer, Michelle D. Watkinson, John Nury, Elise Barberis, Molly C. Mack   +5 more
wiley   +1 more source

Skill set profile clustering: the empty K-means algorithm with automatic specification of starting cluster centers [PDF]

, 2010
While students’ skill set profiles can be estimated with formal cognitive diagnosis models [8], their computational complexity makes simpler proxy skill estimates attractive [1, 4, 6].
Nugent, R., Ayers, E., Dean, N.
core   +4 more sources