Summary: | The data mining or a knowledge discovery from data becomes more significant these days. Our world faces an enormous amount of data which are produced every day. It is important to use clever softwares to help companies sort the information and use them in a right way. Regarding the world areas it describes which methods are used and briefly describes the most important of them. Overall it contains the overview of 33 methods. Regarding the Czech Republic it is based on revisions of 42 articles which are focused on the application of data mining methods. The result from the worldrevision of data mining methods are decision tree, including C4.5 decision tree and classification and regression tree, genetic algorithm, k-nearest neighbor, multilayer perceptron neural network, Naïve Bayes, support vector machine, association rule, expectation maximization and k-means. For the Czech Republic mix of methods were found and they went through the whole spectrum of all areas from business, environment to healhtcare. Between main methods which are used most often are decision tree and its variations, classification and regression trees, genetic algorithm, neural network or logistic regression. The result of the comparison of all the data mining methods used in the Czech Republic and in the world is satisfactory – the same main methods are used in the Czech Republic as well as in the rest of the world. The purpose of this article is to provide the overview of commonly used methods in data mining in different areas in the Czech Republic and in the world. Another goal of this article is to provide an overview of the various areas where data mining is used with regard to the amount of resources dealing with the use of data mining methods in the Czech Republic and in the world. The conducted research made by authors shows a trend in a growth of new Publisher articles in the Czech Republic. The research also shows that the most popular spheres where data mining methods could be used are business, environment and educational sectors.
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