Shakil Ahmed 0002

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4ranked-venue papers

2as first author

0since 2021 · last 2006

0000-0001-6366-4881ORCID · corroborated

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

Databases, data management, data science and information retrieval · 4 · 2 first-authorArtificial intelligence and machine learning · 2 · 1 first-author

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

2 papers

Data mining · 94% Indexing and storage engines · 6%

Topics — the 5 heaviest of 6, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Data mining › pattern mining association rule mining	0.1	2	2004	Data Structure for Association Rule Mining: T-Trees and P-Trees · IEEE Trans. Knowl. Data Eng. 2004 T-Trees, Vertical Partitioning and Distributed Association Rule Mining · ICDM 2003
Data mining › pattern mining › itemset mining frequent itemset mining	0.1	2	2004	Data Structure for Association Rule Mining: T-Trees and P-Trees · IEEE Trans. Knowl. Data Eng. 2004 T-Trees, Vertical Partitioning and Distributed Association Rule Mining · ICDM 2003
Data mining › pattern mining › association rule mining distributed association rule mining	0.0	1	2003	T-Trees, Vertical Partitioning and Distributed Association Rule Mining · ICDM 2003
Data mining › pattern mining › association rule mining parallel association rule mining	0.0	1	2003	T-Trees, Vertical Partitioning and Distributed Association Rule Mining · ICDM 2003
Data mining pattern mining	0.0	1	2003	T-Trees, Vertical Partitioning and Distributed Association Rule Mining · ICDM 2003

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

t-tree · 0.1p-tree · 0.0vertical partitioning · 0.0apriori-t · 0.0

Year	Publication	Venue	Position
2006	Tree-based partitioning of date for association rule mining Shakil Ahmed 0002, Frans Coenen, Paul H. Leng	Knowl. Inf. Syst.	1
2004	A Tree Partitioning Method for Memory Management in Association Rule Mining Shakil Ahmed 0002, Frans Coenen, Paul H. Leng	DaWaK	1
2004	Data Structure for Association Rule Mining: T-Trees and P-Trees abstract Two new structures for association rule mining (ARM), the T-tree, and the P-tree, together with associated algorithms, are described. The authors demonstrate that the structures and algorithms offer significant advantages in terms of storage and execution time. Frans Coenen, Paul H. Leng, Shakil Ahmed 0002	IEEE Trans. Knowl. Data Eng.	3
2003	T-Trees, Vertical Partitioning and Distributed Association Rule Mining abstract We consider a technique (DATA-VP) for distributed (and parallel) association rule mining that makes use of a vertical partitioning technique to distribute the input data, amongst processors. The proposed vertical partitioning is facilitated by a novel compressed set enumeration tree data structure (the T-tree), and an associated mining algorithm (Apriori-T), that allows for computationally effective distributed/parallel ARM when compared with existing approaches. Frans Coenen, Paul H. Leng, Shakil Ahmed 0002	ICDM	3