EDBT 2026 Demo / reviewers in the wild / expert

Jenq-Foung JF Yao

dblp:98/6849 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2003
—ORCID · none

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

Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 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
Algorithms and data structures · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining › pattern mining › tree mining
frequent subtree mining		 0.012003
Efficient Data Mining for Maximal Frequent Subtrees · ICDM 2003
Data mining
pattern mining		 0.012003
Efficient Data Mining for Maximal Frequent Subtrees · ICDM 2003

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

data structure compression · 0.1candidate generation · 0.1
YearPublicationVenuePosition
2003 Efficient Data Mining for Maximal Frequent Subtrees
abstract
A new type of tree mining is defined, which uncovers maximal frequent induced subtrees from a database of unordered labeled trees. A novel algorithm, PathJoin, is proposed. The algorithm uses a compact data structure, FST-Forest, which compresses the trees and still keeps the original tree structure. PathJoin generates candidate subtrees by joining the frequent paths in FST-Forest. Such candidate subtree generation is localized and thus substantially reduces the number of candidate subtrees. Experiments with synthetic data sets show that the algorithm is effective and efficient.
Yongqiao Xiao, Jenq-Foung JF Yao, Zhigang Li 0001, Margaret H. Dunham
 ICDM2