Data Preprocessing

Lecturer:

Tutor:

Supervisor:

Department of Applied Mathematics

Synopsis:

Students learn to prepare raw data for further processing and analysis. They learn what algorithms can be used to extract parameters from various data sources, such as images, texts, time series, etc., and learn the skills to apply these theoretical concepts to solve a specific problem in individual projects - e.g., parameter extraction from image data or from Internet.

Requirements:

Fundamentals of statistics, FCD course in data mining.

Syllabus of lectures:

1. Data exploration, exploratory analysis techniques, visualization of raw data.

2. Descriptive statistics.

3. Methods to determine the relevance of features.

4. Problems with data ? dimensionality, noise, outliers, inconsistency, missing values, non-numeric data.

5. Data cleaning, transformation, imputing, discretization, binning.

6. Reduction of data dimension.

7. Reduction of data volume, class balancing.

8. Feature extraction from text.

9. Feature extraction from documents, web. Preprocessing of structured data.

10. Feature extraction from time series.

11. Feature extraction from images.

12. Data preparation case studies.

13. Automation of data preprocessing.

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-13: 978-3319102467

[4] Fernández, A., García, S., Galar, M., Prati, R. C., Krawczyk, B., Herrera, F., Learning from Imbalanced Data Sets, Springer, 2018, ISBN-13: 978-3319980737

[5] Blokdyk, G., Data pre-processing, 2nd edition, CreateSpace Independent Publishing Platform, 2018, ISBN-13: 978-1987493245

[6] Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A., Feature Selection for High-Dimensional Data (Artificial Intelligence: Foundations, Theory, and Algorithms), 1st edition, 2015, ISBN-13: 978-3319218571

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans:

• Knowledge Engineering, in Czech, Presented in Czech, for Students who Enrolled in 2015 (compulsory course of the specialization, elective course)
• Master Informatics, Presented in Czech, Version for Students who Enrolled in 2015 (VO, elective course)
• Specialization Web and Software Engineering, in Czech, Version 2016 to 2019 (elective course)