Results 61 to 70 of about 14,282,599 (300)
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
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
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 +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 +2 more
openaire +2 more sources
, 2021 The table of GECO scores generated after clustering with k-means using each value of k listed in the first column ‘k’. (CSV)
Akul Singhania (502117) +3 more
core +1 more source
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 +5 more
wiley +1 more source