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Comparation of Models for Datamining
 Gabriš, Ondrej ; Stryka, Lukáš (referee) ; Burgetová, Ivana (advisor)
Increasing development of information technology causes the amount of produced data to grow continously. And so the need becomes more intensive to process the produced data fast and efficiently to discover hidden knowledge contained in the data. This thesis examines the process of knowledge discovery in data, it's particular phases, various methods for mining the data and their comparation. Models of regression, neural network and decision tree are analysed in detail. The thesis also introduces one of the leading tools for datamining the SAS Enterprise Miner and demonstrates it's practical application on data. The purpose of this thesis is comparation of models for datamining in the SAS Enterprise Miner environment, discussion of the results and analysis to determine which model is suitable for different kinds of mined data.
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