Kralj, Petra, Lavrac, Nada, Webb, Geoffrey I.
This paper gives a survey of contrast set mining (CSM), emerging pattern mining (EPM), and subgroup discovery (SD) in a unifying framework named supervised descriptive rule discovery. While all these...
CSM-SD: Methodology for contrast set mining through subgroup discovery (2009)
Kralj, Petra, Lavrac, Nada, Gamberger, Dragan, Krstačić, Antonija
This paper addresses a data analysis task, known as contrast set mining, whose goal is to find differences between contrasting groups. As a methodological novelty, it is shown that this task can be...
Kralj, Petra, Lavrac, Nada, Webb, Geoff
This paper gives a survey of contrast set mining (CSM), emerging pattern mining (EPM), and subgroup discovery (SD) in a unifying framework named supervised descriptive rule discovery. While all these...
CSM-SD: Methodology for contrast set mining through subgroup discovery (2009)
Kralj, Petra, Lavrac, Nada, Krstacic, Antonija
CSM-SD: Methodology for contrast set mining through subgroup discovery
Classi cation Rule Learning with APRIORI-C (2008)
Abstract. Mining of association rules became one of the strongest elds of data mining. This paper presents a classi cation rule learning algorithm APRIORI-C, upgrading APRIORI to dealing with classi...
Relational Descriptive Analysis of Gene Expression Data (2008)
Igor Trajkovski A, Filip Zelezny, Nada Lavrac, Jakub Tolar
Abstract. This paper presents a method that uses gene ontologies, together with the paradigm of relational subgroup discovery, to help find description of groups of genes differentialy expressed in...
Nada Lavrac, Hiroshi Motoda, Tom Fawcett
Abstract. The architecture of Blue Martini Software’s e-commerce suite has supported data collection, transformation, and data mining since its inception. With clickstreams being collected at the...
Closed Sets for Labeled Data (2008)
Garriga, Gemma, Kralj, Petra, Lavrac, Nada
Closed sets have been proven successful in the context of compacted data representation for association rule learning. However, their use is mainly descriptive, dealing only with unlabeled data. This...
FUZZY CLUSTERING OF DOCUMENTS (2008)
This paper presents a short overview of methods for fuzzy clustering and states desired properties for an optimal fuzzy document clustering algorithm. Based on these criteria we chose one of the...
Ripple down rule learning for automated word lemmatisation (2008)
Joël, Plisson, Lavrac, Nada, Mladenić, Dunja, Tomaz, Erjavec
Ripple down rule learning for automated word lemmatisation.
Advancing topic ontology learning through term extraction. (2008)
Fortuna, Blaz, Lavrac, Nada, Velardi, Paola
Advancing topic ontology learning through term extraction.
Closed Sets for Labeled Data (2008)
Garriga, Gemma, Kralj, Petra, Lavrac, Nada
Closed sets have been proven successful in the context of compacted data representation for association rule learning. However, their use is mainly descriptive, dealing only with unlabeled data. This...
Instance selection is an important part of the KDD process. It is aimed at finding a representative data subset that can replace the original dataset, still solving a data mining task as if the whole...
Dragan Gamberger, Nada Lavrac, Ciril Groselj
Diagnostic rules of increased reliability for critical medical applications
Relational Data Mining and Subgroup Discovery (2007)
Abstract. In Inductive Logic Programming (ILP), the recent shift of attention from program synthesis to knowledge discovery resulted in advanced relational data mining techniques that are practically...
Local Patterns: Theory and Practice of Constraint-Based Relational Subgroup Discovery (2007)
Nada Lavrac, Filip Zelezny, Saso Dzeroski
This paper investigates local patterns in the multi-relational constraint-based data mining framework. Given this framework, it contributes to the theory of local patterns by providing the definition...
Editorial: Data Mining Lessons Learned (2004)
Nada Lavrac, Hiroshi Motoda, Tom Fawcett
Introduction Data mining is concerned with finding interesting patterns in data. Many techniques have emerged for analyzing and visualizing large volumes of data. What one finds in the technical...
Decision Support through Subgroup Discovery: Three case studies and the lessons learned (2004)
Nada Lavrac, Dragan Gamberger, Peter Flach
This paper presents ways to use subgroup discovery to generate actionable knowledge for decision support. Actionable knowledge is explicit symbolic knowledge, typically presented in the form of...
S.: Using Constraints in Relational Subgroup Discovery (2003)
Filip Zelezny, Nada Lavrac, Saso Dzeroski
Relational rule learning is typically used in solving classification and prediction tasks. However, it can also be adapted to the description task of subgroup discovery. This paper takes a...
Comparative Evaluation of Approaches to Propositionalization (2003)
Mark-A. Krogel, Simon Rawles, Filip Zelezny, Peter A. Flach, Nada Lavrac, Stefan Wrobel
Propositionalization has already been shown to be a particularly promising approach for robustly and e#ectively handling relational data sets for knowledge discovery. In this paper, we compare...
Comparative Evaluation of Approaches to Propositionalization (2003)
Mark-A. Krogel, Simon Rawles, Filip Zelezny, Peter A. Flach, Nada Lavrac, Stefan Wrobel
Propositionalization has already been shown to be a promising approach for robustly and e#ectively handling relational data sets for knowledge discovery. In this paper, we compare up-to-date methods...
Expert-Guided Subgroup Discovery: Methodology and Application (2002)
This paper presents an approach to expert-guided subgroup discovery. The main step of the subgroup discovery process, the induction of subgroup descriptions, is performed by a heuristic beam search...
Nada Lavrac, Nada Lavrač, Peter Flach, Peter Flach
Inductive Logic Programming (ILP) is concerned with learning relational descriptions that typically have the form of logic programs. In a transformation approach, an ILP task is transformed into an...
A Workshop At, Nada Lavrac, Branko Kavsek (eds.), Silvia Miksch, Branko Kavsek
ion and representation of repeated patterns in high-frequency data ::::::::::::32 8. Y.-L. O: Analysis of primary care data :::::::::::::::::::::::::::::::::::::::::::::::::::::::::40 9. K.M. de...
Inconsistency Tests for Patient Records in a Coronary Heart Disease Database (2000)
Dragan Gamberger, Nada Lavrac, Goran Krstacic, Tomislav Smuc
. The work presents the results of inconsistency detection experiments on the data records of an atherosclerotic coronary heart disease database collected in the regular medical practice. Medical...
The Role of Feature Construction in Inductive Rule Learning (2000)
. This paper proposes a unifying framework for inductive rule learning algorithms. We suggest that the problem of constructing an appropriate inductive hypothesis (set of rules) can be broken down in...
Intelligent Data Analysis in Medicine (2000)
Nada Lavrac, Elpida Keravnou, Blaz Zupan
Excessive amounts of knowledge and data stored in medical databases request the development of specialized tools for storing and accessing of data, data analysis, and effective use of stored...
A report on experiments with weighted relative accuracy in CN2 (2000)
Ljupco Todorovski, Ljupco Todorovski, Peter Flach, Peter Flach, Nada Lavrac, Nada Lavrac
In this report, the prediction performances of two rule evaluation measures, accuracy and weighted relative accuracy, are compared.
Of Weighted Relative, Ljupco Todorovski, Peter Flach, Nada Lavrac
Weighted relative accuracy was proposed in #4# as an alternative to classi#cation accuracy typically used in inductive rule learners.
An Extended Transformation Approach to Inductive Logic Programming (2000)
this paper we show how this limitation can be overcome, by systematic first-order feature construction using a particular individual-centered feature bias. The approach can be applied in any domain...
Rule Evaluation Measures: A Unifying View (1999)
Nada Lavrac, Peter Flach, Blaz Zupan
Numerous measures are used for performance evaluation in machine learning. In predictive knowledge discovery, the most frequently used measure is classi cation accuracy. With new tasks being...
Experiments with noise filtering in a medical domain (1999)
Dragan Gamberger, Nada Lavrac, Ciril Groselj
The paper presents a series of noise detection experiments in a medical problem of coronary artery disease diagnosis. The following algorithms for noise detection and elimination are tested: a...
High Confidence Association Rules for Medical Diagnosis (1999)
Dragan Gamberger, Nada Lavrac, Viktor Jovanoski
This paper elaborates a simple and general decision model based on the so-called confirmation rules. Confirmation rules are generated separately for each diagnostic class so that selected rules cover...
Feature Subset Selection in Association Rules Learning Systems (1999)
Viktor Jovanoski Nada, Nada Lavrac
Mining of association rules is a field of data mining that has received a lot of attention in recent years. Its main advantage over other machine learning tehniques is a low number of database passes...
Computational Logic and Machine Learning: A Roadmap for Inductive Logic Programming (1998)
Computational logic has already significantly influenced (symbolic) machine learning through the field of inductive logic programming (ILP) which is concerned with the induction of logic programs...
Computational Logic and Machine Learning: A Roadmap for Inductive Logic Programming (1998)
Computational logic has already significantly influenced (symbolic) machine learning through the field of inductive logic programming (ILP) which is concerned with the induction of logic programs...
Intelligent Data Analysis in Medicine and Pharmacology (1997)
Nada Lavrac, Elpida Keravnou, Blaz Zupan (eds.), Blaz Zupan
: Anaplastic thyroid carcinoma is a rare but very aggressive tumor. Many factors that might influence the survival of patients have been suggested. The aim of our study was to determine which of the...
Conditions for Occam's Razor Applicability and Noise Elimination (1997)
Dragan Gamberger And, Dragan Gamberger, Nada Lavrac
. The Occam's razor principle suggests that among all the correct hypotheses, the simplest hypothesis is the one which best captures the structure of the problem domain and has the highest...
Igor Zelic, Igor Kononenko, Nada Lavrac, Vamja Vuga
Machine learning techniques can be used to extract knowledge from data stored in medical databases. In our application, various machine learning algorithms were used to extract diagnostic knowledge...
Normal forms for Inductive Logic Programming (1997)
Nada Lavrac, Saso Dzeroski (eds, Peter A. Flach
. In this paper we study induction of unrestricted clausal theories from interpretations. First, we show that in the propositional case induction from complete evidence can be seen as an...
Conditions for Occam's Razor Applicability and Noise Elimination (1997)
. The Occam's razor principle suggests that among all the correct hypotheses, the simplest hypothesis is the one which best captures the structure of the problem domain and has the highest...
Noise elimination in inductive concept learning: A case study in medical diagnosis (1996)
Dragan Gamberger, Nada Lavrac, Saso Dzeroski
Abstract. Compression measures used in inductive learners, such as measures based on the MDL (Minimum Description Length) principle, provide a theoretically justified basis for grading candidate...
Multiple Predicate Learning in Two Inductive Logic Programming Settings (1996)
Inductive logic programming (ILP) is a research area which has its roots in inductive machine learning and computational logic. The paper gives an introduction to this area based on a distinction...
Cost-Sensitive Feature Reduction Applied to a Hybrid Genetic Algorithm (1996)
Nada Lavrac, Dragan Gamberger, Peter Turney
. This study is concerned with whether it is possible to detect what information contained in the training data and background knowledge is relevant for solving the learning problem, and whether...
Preprocessing by a cost-sensitive literal reduction algorithm: REDUCE (1996)
Nada Lavrac, Dragan Gamberger, Peter Turney
This study is concerned with whether it is possible to detect what information contained in the training data and background knowledge is relevant for solving the learning problem, and whether...
Noise detection and elimination applied to noise handling in a KRK chess endgame (1996)
. Compression measures used in inductive learners, such as measures based on the MDL (Minimum Description Length) principle, provide a theoretically justified basis for grading candidate hypotheses....
Intelligent Data Analysis in Medicine and Pharmacology (1996)
Nada Lavrac, Elpida Keravnou, Blaz Zupan (eds.), Blaz Zupan
ion and their Limitations 3 1.3 Application Domain and Basic Concepts 5 Application Domain: Monitoring and Therapy Planning of Artificially Ventilated Newborn Infants in NICUs 6 Input and Output 6 A...
Cost-Sensitive Feature Reduction Applied to a Hybrid Genetic Algorithm (1996)
Nada Lavrac, Dragan Gamberger, Peter Turney
. This study is concerned with whether it is possible to detect what information contained in the training data and background knowledge is relevant for solving the learning problem, and whether...
Preprocessing by a cost-sensitive literal reduction algorithm: REDUCE (1996)
Nada Lavrac, Dragan Gamberger, Peter Turney
This study is concerned with whether it is possible to detect what information contained in the training data and background knowledge is relevant for solving the learning problem, and whether...
Multiple Predicate Learning in Two Inductive Logic Programming Settings (1996)
Inductive logic programming (ILP) is a research area which has its roots in inductive machine learning and computational logic. The paper gives an introduction to this area based on a distinction...
Second Generation Knowledge Acquisition Methods and Their Application to Medicine (1992)
First generation expert systems rely on the use of surface knowledge, such as associational or heuristic. This knowledge is typically acquired from domain experts through exhaustive knowledge...
Experiments In Learning Nonrecursive Definitions Of Relations With Linus (1991)
Nada Lavrac, Saso Dzeroski, Marko Grobelnik
Many successful inductive learning systems use a propositional attribute-value language for the representation of training examples and induced concept descriptions. Recent developments are concerned...
Collaboration Opportunity Finder (1970)
Damjan Demsar, Igor Mozetic, Nada Lavrac
We have designed and implemented a software tool coFinder C a collaboration opportunity finder - aimed at facilitating the work of an opportunity broker in Collaborative Networked Organizations. It...