Matzke, M. Comparison of Computational Complexity of Selected Data Mining Algorithms, Diploma Thesis. Brno, 2018. This diploma thesis deals with the comparison of the time complexity and the success of the classification of selected algorithms for mining knowledge from data with focus on neural networks and optimal settings for work execution. In the theoretical part, it is essential to get acquainted with the distribution of algorithms, their functionality and complexity. Then follows the selection of algorithms with focus on neural networks and their settings, especially hidden layers, momentum and learning rate. Another part deals with data used for experimental testing, which are both nominal and numerical data, and also real or generated. Also included is the accuracy of measurement and performance measurement of the two assemblies used to test individual experiments. The third part is the testing of the time complexity and the percentage success of the algorithms and the output especially in graphical form followed by analysis and recommendations from the results with focus on the optimal setting against the automatic and initial settings.
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:429558 |
| Date | January 2018 |
| Creators | Matzke, Miroslav |
| Source Sets | Czech ETDs |
| Language | Czech |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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