Data Preprocessing

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Code Completion Credits Range Language
NI-PDD Z,ZK 5 2P+1C Czech
Garant předmětu:
Marcel Jiřina
Marcel Jiřina
Magda Friedjungová, Marcel Jiřina, Daniel Vašata
Department of Applied Mathematics

Students learn to prepare raw data for further processing and analysis. They learn what algorithms can be used to extract information from various data sources, such as images, texts, time series, etc., and learn the skills to apply these theoretical concepts to solve specific problems in individual projects - e.g., extraction of characteristics from images or from web pages.


Fundamentals of statistics, FCD course in data mining.

The recommended prerequisite is BIE-VZD.

Syllabus of lectures:

1. Introduction, KDDM standards, CRISP-DM, DM software.

2. Visualization and data exploration.

3. Methods for determining the significance of features.

4. Problems in data: preparation, representation, validation, cleaning, missing values, date format, conversion of non-numeric data.

5. Problems in data: discretization / binning, outliers, cluster analysis, false predictors, group balancing, transformation, sampling.

6. Data reduction: nearest neighbor rule, boundaries between groups, CNN, distance graphs, Wilson editing, multi-edit method.

7. Data reduction: class balancing, Tomek links, SMOTE method, extended nearest neighbor rule.

8. Design methods PCA, ICA, LDA.

9. Preprocessing of time series and extraction of features.

10. Text preprocessing and feature extraction.

11. Image preprocessing and feature extraction: image description, filtering, edge detection, Fourier transform.

12. Image preprocessing and feature extraction: edge and area segmentation, description of objects in the image, feature and structural methods.

Syllabus of tutorials:

1. Assignment of course projects.

2. Consultations.

3. Presentation of course projects.

Study Objective:

Data preprocessing is crucial for successful data processing and takes a lot of time - usually more than the data processing itself. Knowledge of algorithms for extraction of parameters from various data sources is a fundamental part of knowledge engineering,

Study materials:

1. Pyle, D. : Data Preparation for Data Mining. Morgan Kaufmann, 1999. ISBN 1558605290.

2. Guyon, I. - Gunn, S. - Nikravesh, M. - Zadeh, L. A. : Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing). Springer, 2006. ISBN 3540354875.

3. García , S. - Luengo, J. - Herrera F. : Data Preprocessing in Data Mining (Intelligent Systems Reference Library). Springer, 2015. ISBN 978-3319102467.

4. Blokdyk, G. : Data pre-processing (2nd Edition). CreateSpace Independent Publishing Platform, 2018. ISBN 978-1987493245.

Further information:
Time-table for winter semester 2024/2025:
Time-table is not available yet
Time-table for summer semester 2024/2025:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2024-04-11
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