[HTML][HTML] On k-means iterations and Gaussian clusters
Nowadays, k-means remains arguably the most popular clustering algorithm (Jain, 2010;
Vouros et al., 2021). Two of its main properties are simplicity and speed in practice. Here …
Vouros et al., 2021). Two of its main properties are simplicity and speed in practice. Here …
Parallel random swap: An efficient and reliable clustering algorithm in Java
Solving large-scale clustering problems requires an efficient algorithm that can also be
implemented in parallel. K-means would be suitable, but it can lead to an inaccurate …
implemented in parallel. K-means would be suitable, but it can lead to an inaccurate …
Performance of parallel K-means algorithms in Java
L Nigro - Algorithms, 2022 - mdpi.com
K-means is a well-known clustering algorithm often used for its simplicity and potential
efficiency. Its properties and limitations have been investigated by many works reported in …
efficiency. Its properties and limitations have been investigated by many works reported in …
The impact of digitization to ensure competitiveness of the Ha'il region to achieve sustainable development goals
The Kingdom of Saudi Arabia is one of the countries that seek to achieve sustainable
development through Vision 2030. The objective of this research is to study the impact of …
development through Vision 2030. The objective of this research is to study the impact of …
Deep behavioural representation learning reveals risk profiles for malignant ventricular arrhythmias
MZH Kolk, DM Frodi, J Langford, TO Andersen… - NPJ digital …, 2024 - nature.com
We aimed to identify and characterise behavioural profiles in patients at high risk of SCD, by
using deep representation learning of day-to-day behavioural recordings. We present a …
using deep representation learning of day-to-day behavioural recordings. We present a …
A semi-supervised sparse K-Means algorithm
We consider the problem of data clustering with unidentified feature quality and when a
small amount of labelled data is provided. An unsupervised sparse clustering method can …
small amount of labelled data is provided. An unsupervised sparse clustering method can …
Fitting a collider in a quantum computer: tackling the challenges of quantum machine learning for big datasets
Current quantum systems have significant limitations affecting the processing of large
datasets with high dimensionality, typical of high energy physics. In the present paper …
datasets with high dimensionality, typical of high energy physics. In the present paper …
Two Medoid-Based Algorithms for Clustering Sets
L Nigro, P Fränti - Algorithms, 2023 - mdpi.com
This paper proposes two algorithms for clustering data, which are variable-sized sets of
elementary items. An example of such data occurs in the analysis of a medical diagnosis …
elementary items. An example of such data occurs in the analysis of a medical diagnosis …
Exposing and explaining fake news on-the-fly
Social media platforms enable the rapid dissemination and consumption of information.
However, users instantly consume such content regardless of the reliability of the shared …
However, users instantly consume such content regardless of the reliability of the shared …
[HTML][HTML] Development of E-Tourism to Achieve Excellence and Sustainable Development in Tourism: Ha'il Region Case Study
Y Bahou, R Triki, MH Maâloul, K Tissaoui - Sustainability, 2024 - mdpi.com
E-tourism is one of the most important levers for social and economic development. The
World Tourism Organization (UNWTO) has declared 2017 the year of sustainable tourism for …
World Tourism Organization (UNWTO) has declared 2017 the year of sustainable tourism for …