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Data Mining
Slezák, Milan ; Hynčica, Ondřej (referee) ; Honzík, Petr (advisor)
The thesis is focused on an introduction of data mining. Data mining is focused on finding of a hidden data correlation. Interest in this area is dated back to the 60th the 20th century. Data analysis was first used in marketing. However, later it expanded to more areas, and some of its options are still unused. One of methodologies is useful used for creating of this process. Methodology offers a concise guide on how you can create a data mining procedure. The data mining analysis contains a wide range of algorithms for data modification. The interest in data mining causes that number of data mining software is increasing. This thesis contains overviews some of this programs, some examples and assessment.
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Citrus in still life (theoretical-practical thesis)
DRÁBKOVÁ, Veronika
Citrus in still life This bachelor´s thesis deals with the images of citruses in a still-life. The theoretical part of the thesis is focused on the treatise of the still-life phenomenon and the analyzing the symbolic importance of citruses at different time period. Also studies and compares interpretations of fruit still-life paintings of selected authors. This thesis presents painters like Girolamo dai Libri, Caravaggio, Willem Kalf, Pieter Claesz, Gustav Courbet, Paul Cézanne, Paul Gauguin, Vincent van Gogh and more. There are introduced contemporary creations of domestic painters such as Jan Mikulka or Hynek Martinec. The practical part mainly includes the preparatory sketches, paintings and two final large-format paintings.
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Effect of chromatic component on function of antipredatory warning signals
Truhlářová, Marie ; Exnerová, Alice (advisor) ; Pipek, Pavel (referee)
Warning coloration is used by prey to signal its unprofitability to potential predators. Warning colours may have different effects on various cognitive processes of predators (innate avoidance, avoidance learning, memory and generalization). Typical colours regarded as aposematic are red, orange and yellow. Red colour is considered to be the most effective signal and has a significant effect on avoidance learning, memory and generalization. Orange also represents an effective warning signal, though it has been studied less. Yellow is effective aposematic stimulus but it has frequently been found less effective compared to red and orange. Warning functions of white, blue, violet and ultraviolet colours were studied less frequently and their role in aposematism is not yet clear. Iridescent coloration might also be an effective warning signal affecting avoidance learning, memory and generalization. In this thesis I present a summary of information with regard to different warning colours and their effect on cognitive processes of predators. Key words: aposematism, warning coloration, cognitive processes, red, orange, yellow, iridescence, avoidance learning, innate avoidance, memory, generalization
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Data Mining
Slezák, Milan ; Hynčica, Ondřej (referee) ; Honzík, Petr (advisor)
The thesis is focused on an introduction of data mining. Data mining is focused on finding of a hidden data correlation. Interest in this area is dated back to the 60th the 20th century. Data analysis was first used in marketing. However, later it expanded to more areas, and some of its options are still unused. One of methodologies is useful used for creating of this process. Methodology offers a concise guide on how you can create a data mining procedure. The data mining analysis contains a wide range of algorithms for data modification. The interest in data mining causes that number of data mining software is increasing. This thesis contains overviews some of this programs, some examples and assessment.
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Porovnání nekomerčních nástrojů pro dolování znalosti z dat pomocí strojového učení
Ondrejka, Petr
This diploma thesis concerns with features and abilities of chosen software tools for data mining. An important part is the selection and evaluation of each criteria by which the comparison of tools is done. The basic issues of data mining and machine learning are determined here. The comparison criteria are chosen with consideration of the necessary operations that are required to realize the whole process of data mining. The criteria are evaluated on the same level of importance, without weighting. The most widely used algorithms for data mining are chosen for the comparison of time and memory demandingness. The result is the description of selected tools based on the comparison criteria and evaluation of these criteria. The comparison results must be understood with respect of testing data, chosen hardware, and the fact that no weighting was used.
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