https://www.fabricionarcizo.com/tags/data-mining/Data MiningHugoBlox Kit (https://hugoblox.com)en-usTue, 26 Aug 2025 00:00:00 +0000https://www.fabricionarcizo.com/media/icon_hu_da05098ef60dc2e7.pnghttps://www.fabricionarcizo.com/tags/data-mining/https://www.fabricionarcizo.com/courses/damin2025/Tue, 26 Aug 2025 00:00:00 +0000https://www.fabricionarcizo.com/courses/damin2025/<h2 id="description"><strong>Description</strong></h2> <p>This course gives an introduction to the field of data mining. The course is relatively practically oriented, focusing on applicable algorithms. Practical exercises will involve both use of a freely available data mining package and individual implementation of algorithms.</p> <p>The course will cover the following main topics:</p> <ul> <li>The data mining process</li> <li>Cluster analysis</li> <li>Data pre-processing</li> <li>Pattern and association mining</li> <li>Classification and prediction</li> </ul> <p>Application examples will be given from domains including demographics, image processing and healthcare.</p> <p>After the course, the student should be able to:</p> <ul> <li>Analyze data mining problems and reason about the most appropriate methods to apply to a given dataset and knowledge extraction need.</li> <li>Implement basic pre-processing, association mining, classification and clustering algorithms.</li> <li>Apply and reflect on advanced pre-processing, association mining, classification and clustering algorithms.</li> <li>Work efficiently in groups and evaluate the algorithms on real-world problems.</li> </ul> <h2 id="staff">Staff</h2> <ul> <li>Course Manager and Teacher: </li> <li>Teaching Assistant: </li> <li>Teaching Assistant: </li> </ul> <h2 id="supporting-materials">Supporting Materials</h2> <ul> <li> </li> <li> </li> </ul>https://www.fabricionarcizo.com/courses/damin2024/Fri, 30 Aug 2024 00:00:00 +0000https://www.fabricionarcizo.com/courses/damin2024/<h2 id="description"><strong>Description</strong></h2> <p>This course gives an introduction to the field of data mining. The course is relatively practically oriented, focusing on applicable algorithms. Practical exercises will involve both use of a freely available data mining package and individual implementation of algorithms.</p> <p>The course will cover the following main topics:</p> <ul> <li>The data mining process</li> <li>Cluster analysis</li> <li>Data pre-processing</li> <li>Pattern and association mining</li> <li>Classification and prediction</li> </ul> <p>Application examples will be given from domains including demographics, image processing and healthcare.</p> <p>After the course, the student should be able to:</p> <ul> <li>Analyze data mining problems and reason about the most appropriate methods to apply to a given dataset and knowledge extraction need.</li> <li>Implement basic pre-processing, association mining, classification and clustering algorithms.</li> <li>Apply and reflect on advanced pre-processing, association mining, classification and clustering algorithms.</li> <li>Work efficiently in groups and evaluate the algorithms on real-world problems.</li> </ul> <h2 id="staff">Staff</h2> <ul> <li>Course Manager and Teacher: </li> <li>Teaching Assistant: </li> </ul> <h2 id="supporting-materials">Supporting Materials</h2> <ul> <li> </li> <li> </li> </ul>