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Julien Lavergne

dblp:74/4416 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2007
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 1Applied, interdisciplinary, general and emerging computing · 1

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
Graph algorithms and graph theory · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining › clustering
document clustering		 0.112007
On building graphs of documents with artificial ants · WWW 2007
Graph algorithms and graph theory › graph theory › dynamic graphs
incremental graph algorithms		 0.112007
On building graphs of documents with artificial ants · WWW 2007

Methods — techniques the papers use, named apart from their topics

swarm intelligence · 0.1ant colony optimization · 0.1
YearPublicationVenuePosition
2007 Incremental Construction of Neighborhood Graphs Using the Ants Self-Assembly Behavior
abstract
In this paper we present a new incremental algorithm for building neighborhood graphs between data. It is inspired from the self-assembly behavior observed in real ants where ants progressively become attached to an existing support and then successively to other attached ants. Each artificial ant represents one data. The way ants move and build a graph depends on the similarity between the data. We have compared our results to those obtained by the relative neighborhood algorithm on several databases (either artificial or real), and we show that our method is competitive especially with respect to execution times.
Julien Lavergne, Hanene Azzag, Christiane Guinot, Gilles Venturini
 ICTAI (1)1
2007 On building graphs of documents with artificial ants
abstract
We present an incremental algorithm for building a neighborhood graph from a set of documents. This algorithm is based on a population of artificial agents that imitate the way real ants build structures with self-assembly behaviors. We show that our method outperforms standard algorithms for building such neighborhood graphs (up to 2230 times faster on the tested databases with equal quality) and how the user may interactively explore the graph.
Hanene Azzag, Julien Lavergne, Christiane Guinot, Gilles Venturini
 WWW2