Zobrazeno 1 - 10
of 145
pro vyhledávání: '"Pietracaprina, Andrea"'
Publikováno v:
WADS 2023. Lecture Notes in Computer Science, vol 14079. Springer, Cham
We present approximation algorithms for some variants of center-based clustering and related problems in the fully dynamic setting, where the pointset evolves through an arbitrary sequence of insertions and deletions. Specifically, we target the foll
Externí odkaz:
http://arxiv.org/abs/2302.07771
Center-based clustering is a pivotal primitive for unsupervised learning and data analysis. A popular variant is undoubtedly the k-means problem, which, given a set $P$ of points from a metric space and a parameter $k<|P|$, requires to determine a su
Externí odkaz:
http://arxiv.org/abs/2202.08173
Publikováno v:
Algorithms. 2022; 15(2):52
Metric $k$-center clustering is a fundamental unsupervised learning primitive. Although widely used, this primitive is heavily affected by noise in the data, so that a more sensible variant seeks for the best solution that disregards a given number $
Externí odkaz:
http://arxiv.org/abs/2201.02448
Publikováno v:
In Journal of Parallel and Distributed Computing December 2024 194
The most widely used internal measure for clustering evaluation is the silhouette coefficient, whose naive computation requires a quadratic number of distance calculations, which is clearly unfeasible for massive datasets. Surprisingly, there are no
Externí odkaz:
http://arxiv.org/abs/2003.01430
Given a dataset $V$ of points from some metric space, the popular $k$-center problem requires to identify a subset of $k$ points (centers) in $V$ minimizing the maximum distance of any point of $V$ from its closest center. The \emph{robust} formulati
Externí odkaz:
http://arxiv.org/abs/2002.07463
Diversity maximization is a fundamental problem in web search and data mining. For a given dataset $S$ of $n$ elements, the problem requires to determine a subset of $S$ containing $k\ll n$ "representatives" which minimize some diversity function exp
Externí odkaz:
http://arxiv.org/abs/2002.03175
Center-based clustering is a fundamental primitive for data analysis and becomes very challenging for large datasets. In this paper, we focus on the popular $k$-median and $k$-means variants which, given a set $P$ of points from a metric space and a
Externí odkaz:
http://arxiv.org/abs/1904.12728
Center-based clustering is a fundamental primitive for data analysis and becomes very challenging for large datasets. In this paper, we focus on the popular $k$-center variant which, given a set $S$ of points from some metric space and a parameter $k
Externí odkaz:
http://arxiv.org/abs/1802.09205
An uncertain graph $\mathcal{G} = (V, E, p : E \rightarrow (0,1])$ can be viewed as a probability space whose outcomes (referred to as \emph{possible worlds}) are subgraphs of $\mathcal{G}$ where any edge $e\in E$ occurs with probability $p(e)$, inde
Externí odkaz:
http://arxiv.org/abs/1612.06675