Results 61 to 70 of about 15,644,966 (296)
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 +3 more
core +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Purpose To explore caregivers' experiences and perceptions of the nutritional challenges faced by children and adolescents undergoing cancer treatment, including oral intake and the use of nutritional support. Methods This was a qualitative study based on focus groups with parents and caregivers at a Brazilian pediatric oncology hospital ...
Danyelle Apparecida Balsamão Marques +5 more
wiley +1 more source
K−Means Clustering Microaggregation for Statistical Disclosure Control
, 2012 This paper presents a K-means clustering technique that satisfies the bi-objective function to minimize the information loss and maintain k-anonymity. The proposed technique starts with one cluster and subsequently partitions the dataset into two or more
Abdun Naser Mahmood +5 more
core +1 more source
MODIFIED SELECTION OF INITIAL CENTROIDS FOR K- MEANS ALGORITHM [PDF]
, 2016 This study focuses on the improved initialization of initial centroids instead of random selection for the K-means algorithm. The random selection of initial seeds is a major drawback of the original Kmeans algorithm because it leads to less reliable ...
Gerardo, Bobby +2 more
core +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Heavy menstrual bleeding (HMB) is a common condition among adolescents and young adults (AYAs) and is the leading cause of iron deficiency anemia (IDA) in this population. Growing evidence suggests that social determinants of health (SDOH) influence menstrual bleeding patterns, access to care, and treatment. However, no studies have
Maria Carter‐Febres +6 more
wiley +1 more source
K-means algorithms for functional data [PDF]
Neurocomputing, 2015 Abstract Cluster analysis of functional data considers that the objects on which you want to perform a taxonomy are functions f : X ⊂ R p ↦ R and the available information about each object is a sample in a finite set of points f n = { ( x i , y i ) ∈ X × R } i = 1 n . The aim is to
López García, María Luz +2 more
openaire +2 more sources
Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Introduction Peritoneal dialysis (PD) is an established home‐based kidney replacement therapy (KRT), but its uptake remains low in Japan. We evaluated whether individualized education in a dedicated outpatient clinic was associated with the initiation of PD.
Yasuko Ito +7 more
wiley +1 more source
A K-means Algorithm Based On Feature Weighting
MATEC Web of Conferences, 2018 Cluster analysis is a statistical analysis technique that divides the research objects into relatively homogeneous groups. The core of cluster analysis is to find useful clusters of objects.
Xu Yan +4 more
doaj +1 more source
FEBS Letters, EarlyView.We present robust protocols for the preparation of supported lipid bilayers (SLBs) incorporating either Salmonella smooth LPS or outer membrane vesicles (OMVs). We use a combination of quartz crystal microbalance with dissipation (QCM‐D) and fluorescence microscopy to both characterize the SLBs of various compositions and to probe their interactions ...
Hudson P. Pace +6 more
wiley +1 more source
An Improved K-means Clustering Algorithm
Journal of Networks, 2014 An improved k-means clustering algorithm based on K-MEANS algorithm is proposed. This paper gives an improved traditional algorithm by analyzing the statistical data. After a comparison between the actual data and the simulation data, this paper safely shows that the improved algorithm significantly reduce classification error on the simulation data ...
Xiuchang Huang, Wei Su
openaire +2 more sources