Two programs developed by the authors of the article based on artificial intelligence methods are presented. These programs allow solving hidden patterns discovering problems in statistical data: Augur and iWizard-E. The first is based on association rules mining algorithm, and the second is based on a modified CART decision tree. To increase the reliability of the comparative analysis results, four third-party intelligent systems (Deductor, Orange, KNIME and WizWhy) were used in the study, as well as two sets of statistical data, each of which contains sixteen patterns. A series of seven experiments showed significant superiority of iWizard-E over Augur, which is due to a more advanced iWizard-E algorithm. Представлены две разработанные авторами статьи программы на основе методов искусственного интеллекта, позволяющие решать задачи по выявлению скрытых закономерностей в статистических данных: Авгур и iWizard-E. Первая основывается на алгоритме поиска ассоциативных правил, вторая на модифицированном дереве решений CART. Для повышения достоверности результатов сравнительного анализа в исследовании задействованы четыре сторонние интеллектуальные системы (Deductor, Orange, KNIME и WizWhy), а также два набора статистических данных, каждый из которых содержит по 16 закономерностей. Серия из семи экспериментов показала заметное превосходство iWizard-E над Авгур , что обусловлено более совершенным алгоритмом iWizard-E.
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