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Tariqul Hoque

dblp:94/5850 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2007
—ORCID · none

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

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

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

TopicWeightPapersLastEvidence papers
Data mining › pattern mining › frequent pattern mining
incremental frequent pattern mining
0.112005
CanTree: A Tree Structure for Efficient Incremental Mining of Frequent Patterns · ICDM 2005
Data mining
pattern mining
0.112005
CanTree: A Tree Structure for Efficient Incremental Mining of Frequent Patterns · ICDM 2005

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

canonical-order tree · 0.1FP-tree · 0.1
YearPublicationVenuePosition
2007 CanTree: a canonical-order tree for incremental frequent-pattern mining
Carson K. Leung, Quamrul I. Khan, Tariqul Hoque
Knowl. Inf. Syst.4
2005 CanTree: A Tree Structure for Efficient Incremental Mining of Frequent Patterns
abstract
Since its introduction, frequent-pattern mining has been the subject of numerous studies, including incremental updating. Many existing incremental mining algorithms are Apriori-based, which are not easily adoptable to FP-tree based frequent-pattern mining. In this paper, we propose a novel tree structure, called CanTree (canonical-order tree), that captures the content of the transaction database and orders tree nodes according to some canonical order. By exploiting its nice properties, the CanTree can be easily maintained when database transactions are inserted, deleted, and/or modified. For example, the CanTree does not require adjustment, merging, and/or splitting of tree nodes during maintenance. No rescan of the entire updated database or reconstruction of a new tree is needed for incremental updating. Experimental results show the effectiveness of our CanTree.
Carson K. Leung, Quamrul I. Khan, Tariqul Hoque
ICDM3