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The goal of data mining is to extract knowledge, dependencies and rules from data sets. Many complex
methods were developed to solve it. This thesis presents some of the most important methods, which
include the decision trees with algorithms ID3, C4.5 and CART, neural networks like multilayer neural
networks with the backpropagation algorithm, RBF networks, Kohonens maps and some modifications
of LVQ method. There are also described some clustering methods like hierarchical clustering, QT
clustering, kmeans method and its fuzzy modification. The work also includes data pre-processing
techniques, which are very important in order to obtain better results of data mining process.
Experimental part of the work compares the presented methods by means of the results of many tests on
real-world data sets. The results can be used as a guide to choose an appropriate method and its
parameters for some given data set. In this work there is presented author's implementation of the
decision trees C4.5 and CART in C#. In the application it is possible to watch details of algorithms
work. The application provides an API enabling an implementation of new algorithms.