Demo Data from SIGIR 2026 / reviewers in the wild / expert
Tariqul Hoque
dblp:94/5850
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data mining › pattern mining › frequent pattern mining
incremental frequent pattern mining |
0.1 | 1 | 2005 | CanTree: A Tree Structure for Efficient Incremental Mining of Frequent Patterns · ICDM 2005 |
Data mining
pattern mining |
0.1 | 1 | 2005 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 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 PatternsabstractSince 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 |
ICDM | 3 |