[HTML][HTML] On k-means iterations and Gaussian clusters
RC de Amorim, V Makarenkov - Neurocomputing, 2023 - Elsevier
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 …
[HTML][HTML] MalOSDF: An Opcode Slice-Based Malware Detection Framework Using Active and Ensemble Learning
W Guo, J Xue, W Meng, W Han, Z Liu, Y Wang, Z Li - Electronics, 2024 - mdpi.com
The evolution of malware poses significant challenges to the security of cyberspace.
Machine learning-based approaches have demonstrated significant potential in the field of …
Machine learning-based approaches have demonstrated significant potential in the field of …
A meta-heuristics based framework of cluster label optimization in MR images using stable random walk
V Srivastava, SS Singh - Multimedia Tools and Applications, 2024 - Springer
Cluster labeling is a problem of finding optimal clusters by maximizing the similarity within
clusters. Generally, classical optimization methods are used for clustering, which depends …
clusters. Generally, classical optimization methods are used for clustering, which depends …
Inertia-based indices to determine the number of clusters in K-means: an experimental evaluation
A Rykov, RC De Amorim, V Makarenkov… - IEEE Access, 2024 - ieeexplore.ieee.org
This paper gives an experimentally supported review and comparison of several indices
based on the conventional K-means inertia criterion for determining the number of clusters …
based on the conventional K-means inertia criterion for determining the number of clusters …
An Extensive Empirical Comparison of k-means Initialization Algorithms
S Harris, RC De Amorim - IEEE Access, 2022 - ieeexplore.ieee.org
The k-means clustering algorithm, whilst widely popular, is not without its drawbacks. In this
paper, we focus on the sensitivity of k-means to its initial set of centroids. Since the cluster …
paper, we focus on the sensitivity of k-means to its initial set of centroids. Since the cluster …
[PDF][PDF] Using K-means to Analyze Out-of-Bound Unusual Network Traffic and to Predict Data Theft Malware on a Hospital Network
Z Pu, Y Tong, Q Ji, H Liu - osf.io
K-means clustering for anomaly detection in hospital networks represents a novel and
significant approach due to its ability to effectively identify unusual patterns indicative of …
significant approach due to its ability to effectively identify unusual patterns indicative of …