Results 41 to 50 of about 938,196 (259)
Patient Data Analysis with the Quantum Clustering Method
Quantum Reports, 2023 Quantum computing is one of the most promising solutions for solving optimization problems in the healthcare world. Quantum computing development aims to light up the execution of a vast and complex set of algorithmic instructions. For its implementation,
Shradha Deshmukh +2 more
doaj +1 more source
Randomized Dimensionality Reduction for k-means Clustering [PDF]
, 2013 We study the topic of dimensionality reduction for $k$-means clustering. Dimensionality reduction encompasses the union of two approaches: \emph{feature selection} and \emph{feature extraction}.
Boutsidis, Christos +3 more
core
Fast k-means based on KNN Graph
, 2017 In the era of big data, k-means clustering has been widely adopted as a basic processing tool in various contexts. However, its computational cost could be prohibitively high as the data size and the cluster number are large.
Deng, Cheng-Hao, Zhao, Wan-Lei
core +1 more source
Parent‐to‐Child Information Disclosure in Pediatric Oncology
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Despite professional consensus regarding the importance of open communication with pediatric cancer patients about their disease, actual practice patterns of disclosure are understudied. Extant literature suggests a significant proportion of children are not told about their diagnosis/prognosis, which is purported to negatively ...
Rachel A. Kentor +12 more
wiley +1 more source
Clustering Library Loan Books Using K-Means Clustering
Journal of Information Systems and InformaticsOptimal library collection management requires an understanding of book borrowing patterns to align availability with user needs. Without proper analysis, less popular books may remain in large quantities, while popular books may experience shortages ...
Mawar Indah Tanjung, Sriani Sriani
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Performance characterization of clustering algorithms for colour image segmentation [PDF]
, 2006 This paper details the implementation of three traditional clustering techniques (K-Means clustering, Fuzzy C-Means clustering and Adaptive K-Means clustering) that are applied to extract the colour information that is used in the image segmentation ...
Ghita, Ovidiu +2 more
core
Pediatric Blood &Cancer, EarlyView.ABSTRACT Objectives To identify predictors of chronic ITP (cITP) and to develop a model based on several machine learning (ML) methods to estimate the individual risk of chronicity at the timepoint of diagnosis. Methods We analyzed a longitudinal cohort of 944 children enrolled in the Intercontinental Cooperative immune thrombocytopenia (ITP) Study ...
Severin Kasser +6 more
wiley +1 more source
Face detection based on K-medoids clustering and associated with convolutional neural networks
HeliyonOver the last several years, the COVID-19 epidemic has spread over the globe. People have become used to the novel standard, which involves working from home, chatting online, and keeping oneself clean, to stop the spread of COVID-19.
Potharla Ramadevi, Raja Das
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Analyze the Clustering and Predicting Results of Palm Oil Production in Aceh Utara
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2023 PT. Perkebunan Nusantara 1 is engaged in oil palm production with a total land area of 1,144 Ha. The formulation of this research can determine productive land clusters based on land area, number of trees, number of stages, and palm oil production ...
Mutammimul Ula +3 more
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Balanced K-Means for Clustering [PDF]
, 2014 We present a k-means-based clustering algorithm, which optimizes mean square error, for given cluster sizes. A straightforward application is balanced clustering, where the sizes of each cluster are equal. In k-means assignment phase, the algorithm solves the assignment problem by Hungarian algorithm.
Malinen Mikko, Fränti Pasi
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