Objectives and outcomes
Acquisition of theoretical knowledge and practical skills in the field of data mining. Students understand
the basic concepts, principles and methods of data mining and are able to apply various techniques of
data mining implementation to solving real problems.
Lectures
The concept of data mining and areas of application. Connection with other areas of computer science –
machine learning, databases, data analytics, statistics. Stages in a data mining project. Data preparation
– cleaning, transformation, reduction, discretszation and generation of concept hierarchy. Presentation of
knowledge. Attribute-oriented analysis. Data mining algorithms – association rules, classification,
prediction. Clustering. Advanced data mining techniques – text mining, Bayesian approach to text
classification. Web mining – classification of web pages, knowledge extraction from web pages.
Practical classes
Illustration of preparing data for processing on a selected real data set in a specialised development
environment for data mining. Overview of the data filtering, transformation and discretisation process.
Display of data visualisation in the selected development environment. Application of statistical
calculations over a selected set of data. Illustrative examples of the use of association rules. Review of
representative examples of data mining techniques: classification (decision tree), prediction and
clustering. Testing selected techniques. Development of a project with the application of various data
mining techniques on real data and extraction of the most accurate data model possible.