Publications related to DBSCAN

Neural 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 ...

SPRINGERNATURE2023

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Mehmet Fatih Sahin

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

EPFL2023

Single-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, Cham2022

Linearization 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 Publishing2022

Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap

Florent Gérard Krzakala, Lenka Zdeborová, Luca Pesce, Bruno Loureiro, Bruno Loureiro

A simple model to study subspace clustering is the high-dimensional k -Gaussian mixture model where the cluster means are sparse vectors. Here we provide an exact asymptotic characterization of the statistically optimal reconstruction error in this model i ...

2022

Discriminative 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 ...

2022

Size and duration of COVID-19 clusters go along with a high SARS-CoV-2 viral load: A spatio-temporal investigation in Vaud state, Switzerland

Stéphane Joost, Séverine Vuilleumier Varisco, Anaïs Laurence Ladoy, Idris Guessous, Onya Opota

To understand the geographical and temporal spread of SARS-CoV-2 during the first documented wave of infection in the state of Vaud, Switzerland, we analyzed clusters of positive cases using the precise residential location of 33,651 individuals tested (RT ...

2021

On Perfect Clustering of High Dimension, Low Sample Size Data

Soham Sarkar

Popular clustering algorithms based on usual distance functions (e.g., the Euclidean distance) often suffer in high dimension, low sample size (HDLSS) situations, where concentration of pairwise distances and violation of neighborhood structure have advers ...

IEEE COMPUTER SOC2020

Multispectroscopic Study of Single Xe Clusters Using XFEL Pulses

Christoph Bostedt, Christophe Nicolas

X-ray free-electron lasers (XFELs) deliver ultrashort coherent laser pulses in the X-ray spectral regime, enabling novel investigations into the structure of individual nanoscale samples. In this work, we demonstrate how single-shot small-angle X-ray scatt ...

2019

Graph 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

Spherical Clustering of Users Navigating 360° Content

Pascal Frossard, Francesca De Simone, Laura Toni

In Virtual Reality (VR) applications, understanding how users explore the omnidirectional content is important to optimize content creation, to develop user-centric services, or even to detect disorders in medical applications. Clustering users based on th ...

IEEE2019

Overlapping spatial clusters of sugar-sweetened beverage intake and body mass index in Geneva state, Switzerland

Stéphane Joost, Idris Guessous, David Nicolas De Ridder, Beatrice Bacchilega

Background: Obesity and obesity-related diseases represent a major public health concern. Recently, studies have substantiated the role of sugar-sweetened beverages (SSBs) consumption in the development of these diseases.The fine identification of populati ...

Springer2019

Fast Approximate Spectral Clustering for Dynamic Networks

Pierre Vandergheynst, Lionel Jérémie Martin, Andreas Loukas

Spectral clustering is a widely studied problem, yet its complexity is prohibitive for dynamic graphs of even modest size. We claim that it is possible to get information from past cluster assignments to expedite computation. Our approach builds on a recen ...

2018

Application of Machine Learning and Mathematical Programming in the Optimization of the Energy Management System for Hybrid-Electric Vessels Having Cyclic Operations

Francesco Baldi, Navid Mohammadzadeh

Shipping contributes today to 2.1% of global anthropogenic greenhouse gas emissions and its share is expected to grow in the coming years. At the same time, fuel prices are increasing and companies of the related increase in operational costs. This demands ...

2018

Generative Models for Learning Robot Manipulation Skills from Humans

Ajay Kumar Tanwani

A long standing goal in artificial intelligence is to make robots seamlessly interact with humans in performing everyday manipulation skills. Learning from demonstrations or imitation learning provides a promising route to bridge this gap. In contrast to d ...

EPFL2018

Robust and Efficient Data Clustering with Signal Processing on Graphs

Lionel Jérémie Martin

Data is pervasive in today's world and has actually been for quite some time. With the increasing volume of data to process, there is a need for faster and at least as accurate techniques than what we already have. In particular, the last decade recorded t ...

EPFL2018

Accelerated Spectral Clustering Using Graph Filtering of Random Signals

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We build upon recent advances in graph signal processing to propose a faster spectral clustering algorithm. Indeed, classical spectral clustering is based on the computation of the first

k

eigenvectors of the similarity matrix' Laplacian, whose computati ...

Ieee2016

On characterizing protein spatial clusters with correlation approaches

Aleksandra Radenovic, Jayakrishnan Unnikrishnan, Arun Shivanandan

Spatial aggregation of proteins might have functional importance, e.g., in signaling, and nano-imaging can be used to study them. Such studies require accurate characterization of clusters based on noisy data. A set of spatial correlation approaches free o ...

Nature Research2016

ASTErIsM: application of topometric clustering algorithms in automatic galaxy detection and classification

Jean-Paul Richard Kneib, Frédéric Courbin, Danuta Janina Paraficz

We present a study on galaxy detection and shape classification using topometric clustering algorithms. We first use the DBSCAN algorithm to extract, from CCD frames, groups of adjacent pixels with significant fluxes and we then apply the DENCLUE algorithm ...

Oxford Univ Press2016

Nmf-Based Blind Source Separation Using A Linear Predictive Coding Error Clustering Criterion

Xin Guo

Non-negative matrix factorization (NMF) based sound source separation involves two phases: First, the signal spectrum is decomposed into components which, in a second step, are clustered in order to obtain estimates of the source signal spectra. The major ...

Ieee2015