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Comparing methods of knowledge discovery from data
Jungmannová, Iva ; Ivánek, Jiří (advisor) ; Dvořák, Jan (referee)
(in English): The thesis is devoted to the comparison of a few methods of mining knowledge from data. Methods decision tree, classification rules, cluster analysis, and Naive Bayes classifier were applied to the data sample. Data about clients of a non-profit organization Association of Civil Counseling were used. It has been worked according to the technological process of knowledge mining. In the thesis was applied data description, data preparation, modeling and testing and results from interpretation. Because of using the same sample of data and similar data preparation, overlapping results are also expected. The research is focused not only on results similarity, but also differences in results. The correlation between the amount of debt of clients and other attributes was found. In the results, there really were some patterns repeating through most of all methods. It turned out the amount of debt is related to a number of creditors. The more creditors, the higher amount of debt. Clients with a higher amount of liabilities had also higher debt. The results might not be surprising, but it proves the functionality of models and comparability of results.
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