Branko Kavšek

University of Primorska | UP · Department of Information Sciences and Technologies

Word games are one of the most essential factors of vocabulary learning and matching letters to form words for children aged 5–12. These games help children to improve letter and word recognition, memory-building, and vocabulary retention skills. Since Uzbek is a low-resource language, there has not been enough research into designing word games fo...

Machine learning techniques are ever prevalent as datasets continue to grow daily. Associative classification (AC), which combines classification and association rule mining algorithms, plays an important role in understanding big datasets that generate a large number of rules. Clustering, on the other hand, can contribute by reducing the rule spac...

Currently, the computational power present in the sensors forming a wireless sensor network (WSN) allows for implementing most of the data processing and analysis directly on the sensors in a decentralized way. This shift in paradigm introduces a shift in the privacy and security problems that need to be addressed. While a decentralized implementat...

Indoor Air Quality monitoring is a major asset to improving quality of life and building management. Today, the evolution of embedded technologies allows the implementation of such monitoring on the edge of the network. However, several concerns need to be addressed related to data security and privacy, routing and sink placement optimization, prot...

The size of collected data is increasing and the number of rules generated on those datasets is getting bigger. Producing compact and accurate models is being the most important task of data mining.

Building accurate and compact classifiers in real-world applications is one of the crucial tasks in data mining nowadays. In this paper, we propose a new method that can reduce the number of class association rules produced by classical class association rule classifiers, while maintaining an accurate classification model that is comparable to the...

Associative classification is a machine learning approach that aims to build accurate, effective and compact classification models (classifiers) by combining paradigms from classification and association rule mining. Research studies show that associative classification approaches could achieve higher accuracy than some of the traditional classific...

Huge amounts of data are being collected and analyzed nowadays. By using the popular rule-learning algorithms, the number of rule discovered on those ?big? datasets can easily exceed thousands. To produce compact, understandable and accurate classifiers, such rules have to be grouped and pruned, so that only a reasonable number of them are presente...

Huge amounts of data are being collected and analyzed nowadays. By using the popular rule-learning algorithms, the number of rules discovered on those big datasets can easily exceed thousands of rules. To produce compact and accurate classifiers, such rules have to be grouped and pruned, so that only a reasonable number of them are presented to the...

Existing classification rule learning algorithms use mainly greedy heuristic search to find regularities in datasets for classification. In recent years, extensive research on association rule mining was performed in the machine learning community on learning rules by using exhaustive search. The main objective is to find all rules in data that sat...

Stock market analysis is one of the biggest areas of interest for text mining. Many researchers proposed different approaches that use text information for predicting the movement of stock market indices. Many of these approaches focus either on maximising the predictive accuracy of the model or on devising alternative methods for model evaluation....

Terms of service (ToS) are becoming a ubiquitous part of online account creation. There is a general understanding that users rarely read them and do not particularly care about binding themselves into legally enforceable contracts with online service providers. Some services are trying to change this trend with presenting ToS sections as key point...

This paper presents a subgroup discovery algorithm APRIORI-SD, developed by adapting association rule learning to subgroup discovery. This was achieved by building a classification rule learner APRIORI-C, enhanced with a novel post-processing mechanism, a new quality measure for induced rules (weighted relative accuracy) and using probabilistic cla...

The paper presents a way to overcome the shortcomings of traditional learning by enforcing collaboration between students and introducing self-assessment as part of the process of final grade formation. Treating collaboration and self assessment as two elements of a modern learning process that are very closely bounded together, the authors argue t...

In the last years, numerous papers were published comparing different learning management systems (LMS). Some of them dealt with only few comparison criteria, while others included almost every imaginable feature. When faced to do a comparison ourselves, we came across many of such papers and did a research of what authors considered relevant in an...

This paper proposes a selection of knowledge technologies for health care planning and decision support in regional-level management of Slovenian public health care. Data mining and statistical techniques were used to analyze databases collected by a regional Public Heath Institute. Specifically, we addressed the problem of directing patients from...

This paper investigates how to adapt standard classification rule learning approaches to subgroup discovery. The goal of subgroup discovery is to find rules describing subsets of the population that are sufficiently large and statistically unusual. The paper presents a subgroup discovery algorithm, CN2-SD, developed by modifying parts of the CN2 cl...

Rule learning is typically used in solving classification and prediction tasks. However, learning of classification rules can be adapted also to subgroup dis-covery. Such an adaptation has already been done for the CN2 rule learning algorithm. In previous work this new algorithm, called CN2-SD, has been described in detail and applied to the well k...

This paper presents a subgroup discovery algorithm APRIORI-SD, developed by adapting association rule learning to subgroup discovery., This was achieved by building a classification rule learner APRIORI-C, enhanced with a novel post-processing mechanism, a new quality measure for induced rules (weighted relative accuracy) and ;using probabilistic c...

In this chapter we describe our experience with mining a large multi-relational database of traffic accident reports. We applied a range of data mining techniques to this dataset, including text mining, clustering of time series, subgroup discovery, multi-relational data mining, and association rule learning. We also describe a collaborative data m...

Rule 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 me...

Rule learning is typically used in solving classification and prediction tasks. However, learning of classification rules can be adapted also to subgroup discovery. Such an adaptation has already been done for the CN2 rule learning algorithm. In previous work this new algorithm, called CN2-SD, has been described in detail and applied to the well kn...

Decision tree learning is relatively non-robust: a small change in the training set may significantly change the structure of the induced decision tree. This paper presents a decision tree construction method in which the domain model is constructed by consensus clustering of N decision trees induced in N-fold cross-validation. Experimental results...

In data analysis, induction of decision trees serves two main goals: first, induced decision trees can be used for classification/prediction of new instances, and second, they represent an easy-to-interpret model of the problem domain that can be used for explanation. The accuracy of the induced classifier is usually estimated using N-fold cross va...

This paper reports on data mining experiences of the 5th Framework project Data Mining and Decision Support for Business Competitiveness: A European Virtual Enterprise (Sol-Eu-Net). The data mining lessons learned are reported from the following perspectives: application results, business, views of Sol-Eu-Net partners acquired by interview techniqu...

Rule learning is typically used in 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 CN2 rule learning algorithm. Modifications include a new covering algorithm (weighted covering algorithm), a new search heuris...

This dissertation investigates how to adapt standard classification rule learning approaches to subgroup discovery. The goal of subgroup discovery is to find rules describing subsets of a selected population that are sufficiently large and statistically unusual in terms of class distribution. The dissertation presents a subgroup discovery algor...