Add to ORKGThe main aim of the article is to present the modifications of inference algorithms based on information extracted from large rule sets. The article introduces the conception of discovering the knowledge about rules saved in rule bases. It also describes the cluster analysis and decision units conception for this task and presents the optimization of forward and backward inference algorithms as well as selected experimental results
Data mining has become an important research topic. The increasing use of computer results in an exp...
Medical datasets have reached enormous capacities. This data may contain valuable information that a...
Abstract. Machine learning is a very important aspect for improving experts ’ everyday work. Not tha...
Decision support systems founded on rule-based knowledge representation should be equipped with rule...
Abstract. This paper presents new inference algorithms based on rules partition. Optimisation relies...
This dissertation thesis introduces new methods of automated knowledge-base creation and tuning in i...
Data mining, referred to as knowledge discovery in databases (KDD), is the nontrivial process of ide...
In this paper, we present new approach of inference processes for large, complex knowledge bases. No...
In this paper, a Fuzzy Association Rule Mining (FARM) with expert-driven approach is proposed to acq...
The article explores data mining algorithms, which based on rules and calculations, that allow us to...
AbstractIn the performance of data mining and knowledge discovery activities, rough set theory has b...
The topic of this thesis is knowledge discovery and artificial intelligence based knowledge discover...
Abstract: The application of knowledge extraction methodologies in support of medical informatics p...
In this work the topic of applying clustering as a knowledge extraction method from real-world data ...
In the paper a method for designing production rules with uncertainty from medical aggregate data is...
Data mining has become an important research topic. The increasing use of computer results in an exp...
Medical datasets have reached enormous capacities. This data may contain valuable information that a...
Abstract. Machine learning is a very important aspect for improving experts ’ everyday work. Not tha...
Decision support systems founded on rule-based knowledge representation should be equipped with rule...
Abstract. This paper presents new inference algorithms based on rules partition. Optimisation relies...
This dissertation thesis introduces new methods of automated knowledge-base creation and tuning in i...
Data mining, referred to as knowledge discovery in databases (KDD), is the nontrivial process of ide...
In this paper, we present new approach of inference processes for large, complex knowledge bases. No...
In this paper, a Fuzzy Association Rule Mining (FARM) with expert-driven approach is proposed to acq...
The article explores data mining algorithms, which based on rules and calculations, that allow us to...
AbstractIn the performance of data mining and knowledge discovery activities, rough set theory has b...
The topic of this thesis is knowledge discovery and artificial intelligence based knowledge discover...
Abstract: The application of knowledge extraction methodologies in support of medical informatics p...
In this work the topic of applying clustering as a knowledge extraction method from real-world data ...
In the paper a method for designing production rules with uncertainty from medical aggregate data is...
Data mining has become an important research topic. The increasing use of computer results in an exp...
Medical datasets have reached enormous capacities. This data may contain valuable information that a...
Abstract. Machine learning is a very important aspect for improving experts ’ everyday work. Not tha...