Demo Data from VLDB 2026 / reviewers in the wild / expert
Branko Kavsek
dblp:38/2247
· DBLP profile ↗
7ranked-venue papers
2as first author
1since 2021 · last 2021
0000-0002-9092-1633ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 1 first-authorDatabases, data management, data science and information retrieval · 3 · 2 first-authorApplied, interdisciplinary, general and emerging computing · 2 · 1 since 2021Human-computer interaction and ubiquitous 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 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data mining
pattern mining |
0.1 | 2 | 2004 | Subgroup Discovery with CN2-SD · J. Mach. Learn. Res. 2004 Adapting classification rule induction to subgroup discovery · ICDM 2002 |
Data mining › pattern mining
subgroup discovery |
0.1 | 2 | 2004 | Subgroup Discovery with CN2-SD · J. Mach. Learn. Res. 2004 Adapting classification rule induction to subgroup discovery · ICDM 2002 |
Data mining › predictive modeling
classification |
0.0 | 1 | 2002 | Adapting classification rule induction to subgroup discovery · ICDM 2002 |
Data mining › predictive modeling › classification › rule learning
classification rule learning |
0.0 | 1 | 2002 | Adapting classification rule induction to subgroup discovery · ICDM 2002 |
Data mining › predictive modeling › classification
rule learning |
0.0 | 1 | 2002 | Adapting classification rule induction to subgroup discovery · ICDM 2002 |
Methods — techniques the papers use, named apart from their topics
rule induction · 0.0CN2 · 0.0search heuristic · 0.0probabilistic classification · 0.0covering algorithm · 0.0ROC curve · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | ACHC: Associative Classifier Based on Hierarchical Clustering
Jamolbek Mattiev, Branko Kavsek |
IDEAL | 2 |
| 2015 | "I Agree": The Effects of Embedding Terms of Service Key Points in Online User Registration Form
Matjaz Kljun, Jernej Vicic, Klen Copic Pucihar, Branko Kavsek |
INTERACT (2) | 4 |
| 2005 | Resource Modeling and Analysis of Regional Public Health Care Data by Means of Knowledge Technologies
Nada Lavrac, Marko Bohanec, Aleksander Pur, Bojan Cestnik, Mitja Jermol, Tanja Urbancic, Marko Debeljak, Branko Kavsek, Tadeja Kopac |
AIME | 8 |
| 2004 | Subgroup Discovery with CN2-SD
Nada Lavrac, Branko Kavsek, Peter A. Flach, Ljupco Todorovski |
J. Mach. Learn. Res. | 2 |
| 2003 | APRIORI-SD: Adapting Association Rule Learning to Subgroup Discovery
Branko Kavsek, Nada Lavrac, Viktor Jovanoski |
IDA | 1 |
| 2002 | Adapting classification rule induction to subgroup discoveryabstractRule learning is typically used for solving classification and prediction tasks. However learning of classification rules can be adapted also to subgroup discovery. This paper shows how this can be achieved by modifying the covering algorithm and the search heuristic, performing probabilistic classification of instances, and using an appropriate measure for evaluating the results of subgroup discovery. Experimental evaluation of the CN2-SD subgroup discovery algorithm on 17 UCI data sets demonstrates substantial reduction of the number of induced rules, increased rule coverage and rule significance, as well as slight improvements in terms of the area under the ROC curve. Nada Lavrac, Peter A. Flach, Branko Kavsek, Ljupco Todorovski |
ICDM | 3 |
| 2001 | Consensus Decision Trees: Using Consensus Hierarchical Clustering for Data Relabelling and Reduction
Branko Kavsek, Nada Lavrac, Anuska Ferligoj |
ECML | 1 |