| Classification rule learning with APRIORI-C V Jovanoski, N Lavrač Portuguese conference on artificial intelligence, 44-51, 2001 | 137 | 2001 |
| Apriori-sd: Adapting association rule learning to subgroup discovery B Kavšek, N Lavrač, V Jovanoski International Symposium on Intelligent Data Analysis, 230-241, 2003 | 121 | 2003 |
| A study of relevance for learning in deductive databases N Lavrač, D Gamberger, V Jovanoski The Journal of Logic Programming 40 (2-3), 215-249, 1999 | 61 | 1999 |
| Towards actionable cognitive digital twins for manufacturing. JM Rozanec, J Lu, A Kosmerlj, K Kenda, K Dimitris, V Jovanoski, J Rupnik, ... SeDiT@ ESWC 2615, 1-12, 2020 | 35 | 2020 |
| High confidence association rules for medical diagnosis D Gamberger, N Lavrač, V Jovanoski Intelligent Data Analysis in Medicine and Pharmacology IDAMAP'99, a Workshop …, 1999 | 28 | 1999 |
| Qminer: data analytics platform for processing streams of structured and unstructured data B Fortuna, J Rupnik, J Brank, C Fortuna, V Jovanoski, M Karlovcec, ... Software engineering for machine learning workshop, neural information …, 2014 | 19 | 2014 |
| Feature subset selection in association rules learning systems V Jovanoski, N Lavrač | 6 | 1999 |
| Feature SubsetSelection in Association Rules Learning Systems V Nada, N Lavrac Proceeding of Slovenian Electrical and Computer Science Conference, 301-304, 1999 | 2 | 1999 |
| FSADA, an anomaly detection approach V Jovanoski, J Rupnik | | |
| Analizacasovnih zaporedij besedil V Jovanoski | | |
| Analyzing raw log files to find execution anomalies V Jovanoski, J Rupnik, M Karlovcec, B Fortuna | | |
Nada LavracJozef Stefan InstituteVerified email at ijs.si
