Concept
DBSCAN
Publications associées (44)
Recovering Static and Time-Varying Communities Using Persistent Edges
Maximilien Claude Robert Dreveton
This article focuses on spectral methods for recovering communities in temporal networks. In the case of fixed communities, spectral clustering on the simple time-aggregated graph (i.e., the weighted graph formed by the sum of the interactions over all tem ...
Ieee Computer Soc2024Neural ADMIXTURE for rapid genomic clustering
Characterizing the genetic structure of large cohorts has become increasingly important as genetic studies extend to massive, increasingly diverse biobanks. Popular methods decompose individual genomes into fractional cluster assignments with each cluster ...
SPRINGERNATURE2023Single-Trace Clustering Power Analysis of the Point-Swapping Procedure in the Three Point Ladder of Cortex-M4 SIKE
Aymeric Genet, Novak Kaluderovic
In this paper, the recommended implementation of the post-quantum key exchange SIKE for Cortex-M4 is attacked through power analysis with a single trace by clustering with the k-means algorithm the power samples of all the invocations of the elliptic curve ...
Springer, Cham2022Linearization and Identification of Multiple-Attractor Dynamical Systems through Laplacian Eigenmaps
Aude Billard, Bernardo Fichera
Dynamical Systems (DS) are fundamental to the modeling and understanding time evolving phenomena, and have application in physics, biology and control. As determining an analytical description of the dynamics is often difficult, data-driven approaches are ...
MICROTOME PUBL2022Discriminative clustering with representation learning with any ratio of labeled to unlabeled data
We present a discriminative clustering approach in which the feature representation can be learned from data and moreover leverage labeled data. Representation learning can give a similarity-based clustering method the ability to automatically adapt to an ...
2022Graph Spectral Clustering of Convolution Artefacts in Radio Interferometric Images
Paul Hurley, Matthieu Martin Jean-André Simeoni
The starting point for deconvolution methods in radioastronomy is an estimate of the sky intensity called a dirty image. These methods rely on the telescope point-spread function so as to remove artefacts which pollute it. In this work, we show that the in ...
IEEE2019