Grabusts, Pēteris

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Grabusts, Pēteris
Official Name
Grabusts, Pēteris
Alternative Name
Peter Grabusts
Pēteris Grabusts
Main Affiliation
Email
peteris.grabusts@rta.lv
Scopus Author ID
55835004700
Researcher ID
AAU-5594-2021

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  • Publication
    EVOLUTIONARY ALGORITHMS LEARNING METHODS IN STUDENT EDUCATION
    (2021)
    Grabusts, Pēteris 
    ;
    Zorins, Aleksejs 
    Teaching experience shows that during educational process student perceive graphical information better than analytical relationships. As a possible solution, there could be the use of package Matlab in realization of different algorithms for IT studies. Students are very interested in modern data mining methods, such as artificial neural networks, fuzzy logic, clustering and evolution methods. Series of research were carried out in order to demonstrate the suitability of the Matlab for the purpose of visualization of various simulation models of some data mining disciplines – particularly genetic algorithms. Nowadays the possibilities of evolutionary algorithms are widely used in many optimization and classification tasks. There are four paradigms in the world of evolutionary algorithms: evolutionary programming, evolution strategies, genetic algorithms and genetic programming. This paper analyses present-day approaches of genetic algorithms and genetic programming and examines the possibilities of genetic programming that will be used in further research. Genetic algorithm learning methods are often undeservedly forgotten, although the implementation of their algorithms is relatively strong and can be implemented even for students. In the research part of the study the modelling capabilities in data mining studies were demonstrated based on genetic algorithms and real examples. We assume that students already have prior knowledge of genetic algorithms.
  • Publication
    THE POSSIBILITIES OF CLUSTERING LEARNING METHODS IN STUDENT EDUCATION
    (2019)
    Grabusts, Pēteris 
    Many educational courses operate with models that were previously available only in mathematics or other learning disciplines. As a possible solution, there could be the use of package IBM SPSS Statistics and Modeler in realization of different algorithms for IT studies. Series of research were carried out in order to demonstrate the suitability of the IBM SPSS for the purpose of visualization of various simulation models of some data mining disciplines – particularly cluster analysis. Students are very interested in modern data mining methods, such as artificial neural networks, fuzzy logic and clustering. Clustering methods are often undeservedly forgotten, although the implementation of their algorithms is relatively simple and can be implemented even for students. In the research part of the study the modelling capabilities in data mining studies, clustering algorithms and real examples are demonstrated.
  • Publication
    Rule based design using clustering for knowledge acquisition
    (2020-07-20)
    Grabusts, Pēteris 
    Data analysis can be done by expert system decisions on system status according to system input and output data. For the purpose of data analysis, there is often a need to classify data or to find regularities therein. The results of the regularity search can be expressed by the IF-THEN production rules. The use of different approaches – with clustering algorithms, neural networks – makes it possible to obtain rules that characterise data. Knowledge acquisition in this paper is the process of extracting knowledge from numerical data in form of rules. Rules acquisition in this context is based on clustering methods. With the help of the K-means clustering algorithm, rules are derived from trained neural networks. The rule-making methodology is demonstrated on a sample basis of IRIS data. The effectiveness of the obtained rules is evaluated.
  • Publication
    FUZZY LOGIC LEARNING METHODS IN STUDENT EDUCATION
    (2020)
    Grabusts, Pēteris 
    There is a rapidly growing interest in Artificial Intelligence applications in various modern areas. Students are very interested in modern data mining methods such as artificial neural networks, fuzzy logic and clustering. Teaching experience in study work shows that students perceive graphical information better than analytical relationships during learning process. Many training courses operate with models that were previously only available in mathematics disciplines. The solution would be to use the Matlab package to implement different models in Artificial Intelligence areas. Often, an analytical solution or simulation model is much simpler than a visual Matlab model, but it provides an insight into the usefulness of using such models for prospective training purposes. In previous articles, the author has provided examples of how Matlab's capabilities can be used in economic studies, artificial neural networks, and clustering. Fuzzy logic methods are often undeservedly forgotten, although the implementation of their algorithms is relatively simple and can be implemented even for students. In the research part of the study the modelling capabilities in data mining studies are demonstrated with fuzzy logic algorithms and real examples.