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Thomas Manoukian

dblp:48/4389 · DBLP profile ↗
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4ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · none

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

Databases, data management, data science and information retrieval · 4Artificial intelligence and machine learning · 2

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.

Theoretical computer science
1 paper
Algorithms and data structures · 100%
Databases, data mining, and information retrieval
1 paper
Data mining · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithms and data structures › combinatorial algorithms
hypergraph transversals		 0.012003
A Fast Algorithm for Computing Hypergraph Transversals and its Application in Mining Emerging Patterns · ICDM 2003
Data mining › pattern mining › discriminative pattern mining
emerging pattern mining		 0.012003
A Fast Algorithm for Computing Hypergraph Transversals and its Application in Mining Emerging Patterns · ICDM 2003
Data mining
pattern mining		 0.012003
A Fast Algorithm for Computing Hypergraph Transversals and its Application in Mining Emerging Patterns · ICDM 2003

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

minimal transversal computation · 0.1
YearPublicationVenuePosition
2004 Incremental Maintenance on the Border of the Space of Emerging Patterns
Jinyan Li 0001, Thomas Manoukian, Guozhu Dong, Kotagiri Ramamohanarao
 Data Min. Knowl. Discov.2
2003 A Fast Algorithm for Computing Hypergraph Transversals and its Application in Mining Emerging Patterns
abstract
Computing the minimal transversals of a hypergraph is an important problem in computer science that has significant applications in data mining. We present a new algorithm for computing hypergraph transversals and highlight their close connection to an important class of patterns known as emerging patterns. We evaluate our technique on a number of large datasets and show that it outperforms previous approaches by a factor of 9-29 times.
James Bailey 0001, Thomas Manoukian, Kotagiri Ramamohanarao
 ICDM2
2003 Classification Using Constrained Emerging Patterns
James Bailey 0001, Thomas Manoukian, Kotagiri Ramamohanarao
 WAIM2
2002 Fast Algorithms for Mining Emerging Patterns
James Bailey 0001, Thomas Manoukian, Kotagiri Ramamohanarao
 PKDD2