Demo Data from EDBT 2026 / reviewers in the wild / expert
B. Shekar
dblp:88/4468
· DBLP profile ↗
10ranked-venue papers
6as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 10 · 6 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 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
2 papers |
Data mining · 100% |
Topics — the 6 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 |
0.0 | 1 | 2004 | A Transaction-Based Neighbourhood-Driven Approach to Quantifying Interestingness of Association Rules · ICDM 2004 |
Data mining › pattern mining › association rule mining
interestingness measures |
0.0 | 1 | 2004 | A Transaction-Based Neighbourhood-Driven Approach to Quantifying Interestingness of Association Rules · ICDM 2004 |
Data mining
rule ordering |
0.0 | 1 | 2004 | A Transaction-Based Neighbourhood-Driven Approach to Quantifying Interestingness of Association Rules · ICDM 2004 |
Data mining › pattern mining › association rule mining
market basket analysis |
0.0 | 1 | 2004 | A Transaction-Based Neighbourhood-Driven Approach to Quantifying Interestingness of Association Rules · ICDM 2004 |
Data mining
clustering |
0.0 | 1 | 1987 | Pattern Clustering: An Artificial Intelligence Approach · IJCAI 1987 |
Data mining › pattern mining
pattern clustering |
0.0 | 1 | 1987 | Pattern Clustering: An Artificial Intelligence Approach · IJCAI 1987 |
Methods — techniques the papers use, named apart from their topics
relatedness measures · 0.0junction embedding · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Mining Substitution Rules: A Knowledge-based Approach using Dynamic Ontologies
Rupal Sethi, B. Shekar |
ICAART (2) | 2 |
| 2007 | Discovering Mentorship Information from Author Collaboration Networks
V. Suresh, Narayanan Raghupathy, B. Shekar, C. E. Veni Madhavan |
Discovery Science | 3 |
| 2004 | A Transaction-Based Neighbourhood-Driven Approach to Quantifying Interestingness of Association RulesabstractIn this paper, we present a data-driven approach for ranking association rules (ARs) based on interestingness. The occurrence of unrelated or weakly related item-pairs in an AR is interesting. In the retail market-basket context, items may be related through various relationships arising due to mutual interaction, 'substitutability' and 'complementarity.' Item-relatedness is a composite of these relationships. We introduce three relatedness measures for capturing relatedness between item-pairs. These measures use the concept of junction embedding to appropriately weigh the relatedness contributions due to complementarity and substitutability between items. We propose an interestingness coefficient by combining the three relatedness measures. We compare this with two objective measures of interestingness and show the intuitiveness of the proposed interestingness coefficient. B. Shekar, Rajesh Natarajan |
ICDM | 1 |
| 2002 | A Fuzzy Relatedness Measure for Determining Interestingness of Association Rules
B. Shekar, Rajesh Natarajan |
HIS | 1 |
| 2000 | Interrelationships among fuzzy preference-based choice functions and significance of rationality conditions: A taxonomic and intuitive perspective
Pankaj Kulshreshtha, B. Shekar |
Fuzzy Sets Syst. | 2 |
| 1997 | Strategic decision-making processes: network-based representation and stochastic simulation
V. Srinivas, B. Shekar |
Decis. Support Syst. | 2 |
| 1990 | A knowledge-based approach to pattern generation
B. Shekar, M. Narasimha Murty |
Pattern Recognit. | 1 |
| 1989 | Structural aspects of semantic-directed clusters
B. Shekar, M. Narasimha Murty |
Pattern Recognit. | 1 |
| 1987 | Pattern Clustering: An Artificial Intelligence Approach
B. Shekar, M. Narasimha Murty |
IJCAI | 1 |
| 1987 | A knowledge-based clustering scheme
B. Shekar, M. Narasimha Murty |
Pattern Recognit. Lett. | 1 |