1. SylabUZ
2. Faculty of Computer Science, Electrical Engineering and Automatics
3. winter term 2022/2023
4. E-business - First-cycle Erasmus programme
5. Data mining

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Data mining - course description

Aim of the course

Familiarize students with data mining software. Getting to know the data mining methods (data classification, discovering association rules, data clustering) and acquiring the ability to use the learned techniques in practical applications.

Prerequisites

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Scope

Familiarize with data mining software. Types and scales of variables in data mining tasks. Methods of encoding nominal and ordinal variables.

Introduction to data classification. Data classification methods (logistic regression, decision trees, artificial neural networks). Performance metrics. Methods of testing classifiers. Practical exercises on the use of data classification methods.

Introduction to the problem of discovering association rules. Metrics describing the statistical importance and strength of association rules. Shopping cart analysis problem. The computational complexity of the problem of discovering association rules. Apriori algorithm. Practical exercises in the use of methods of discovering association rules.

Introduction to data clustering. Hierarchical clustering and k-means. Similarity measures for data clustering. Practical exercises on the use of data clustering methods.

Teaching methods

Lecture - conventional lecture using a video projector.
Laboratory - practical exercises in the computer laboratory.

 

Learning outcomes and methods of theirs verification

Outcome description	Outcome symbols	Methods of verification	The class form

Assignment conditions

Lecture - the passing criteria is to obtain positive grades from tests carried out at least once in a semester.

Laboratory - the passing criterion is to obtain positive marks for laboratory exercises and tests.

Final mark components = lecture: 50% + teaching laboratory: 50%

Recommended reading

1. James G., Witten D., Hastie T., Tibshirani R., An introduction to statistical learning with applications in R, Springer, 2021.
2. Aggarwal C.C.: Data Mining, Springer, 2015.

Further reading

1. Hastie T., Tibshirani R., Friedman J.H.: The Elements of Statistical Learning, Springer 2001
2. Han J., Kamber, M., Data Mining: Concepts and Techniques, Morgan Kaufmann, 2011.

 

Notes

Modified by dr hab. inż. Marek Kowal, prof. UZ (last modification: 30-04-2022 12:23)