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Etna Lindy

dblp:372/2308 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data mining · 100%
Theoretical computer science
1 paper
Algorithms and data structures · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining
clustering		 0.812024
A (3 + ɛ)-Approximate Correlation Clustering Algorithm in Dynamic Streams · SODA 2024
Data mining › clustering › graph clustering
correlation clustering		 0.812024
A (3 + ɛ)-Approximate Correlation Clustering Algorithm in Dynamic Streams · SODA 2024
Algorithms and data structures › data streams › streaming algorithms
dynamic streams		 0.812024
A (3 + ɛ)-Approximate Correlation Clustering Algorithm in Dynamic Streams · SODA 2024
Algorithms and data structures › data streams
streaming algorithms		 0.812024
A (3 + ɛ)-Approximate Correlation Clustering Algorithm in Dynamic Streams · SODA 2024
Parallel and multicore computing
parallel algorithms		 0.212024
A (3 + ɛ)-Approximate Correlation Clustering Algorithm in Dynamic Streams · SODA 2024
YearPublicationVenuePosition
2024 A (3 + ɛ)-Approximate Correlation Clustering Algorithm in Dynamic Streams
abstract
Grouping together similar elements in datasets is a common task in data mining and machine learning. In this paper, we study streaming and parallel algorithms for correlation clustering, where each pair of elements is labeled either similar or dissimilar. The task is to partition the elements and the objective is to minimize disagreements, that is, the number of dissimilar elements grouped together and similar elements that get separated.
Mélanie Cambus, Fabian Kuhn, Etna Lindy, Shreyas Pai, Jara Uitto
 SODA3