BIE-VZD – Data Mining

Annotation

Students are introduced to the basic methods of discovering knowledge in data. In particular, they learn the basic techniques of data preprocessing, multidimensional data visualization, statistical techniques of data transformation, and fundamental principles of knowledge discovery methods. Students will be aware of the relationships between model bias and variance and will know the fundamentals of assessing model quality. Data mining software is extensively used in the module. Students will be able to apply basic data mining tools to common problems (classification, regression, clustering).

Lectures Program

  1. Cluster analysis.
  2. Introduction to data mining, data preparation, data visualization.
  3. Statistical analysis of data.
  4. Data model, nearest neighbour classifier.
  5. Training, validation and testing, model's quality evaluation.
  6. Artificial neural networks in data mining.
  7. Unsupervised neural networks - competitive learning.
  8. Probability and Bayesian classification.
  9. Decision trees and rules.
  10. Neural networks with supervised learning.
  11. Combining neural networks and models in general.
  12. Data mining in the Clementine environment.
  13. Text mining, Web mining, selected applications, new trends.

Labs Program

  1. Data, visualization, statistics.
  2. Statistical analysis of data.
  3. Data preprocessing, dimension reduction, relevance of inputs.
  4. Model, learning, testing, model validation.
  5. Data mining process, classification, prediction, modeling.
  6. Cluster analysis, SOM.
  7. Project assignment.
  8. [3] Consultations, working on projects.
  9. [3] Presentations of results, workshop.


Last modified: 7.9.2010, 11:09