wydział - prz

Rzeszów University of Technology
Electrical and Computer Engineering
The Faculty of:
Field of study:
Computer Science
Speciality:
MSc
Study degree (BSc, MSc):
COURSE UNIT DESCRIPTION
Knowledge Discovery and Data Mining
Course title:
Lecturer responsible for course:
Contacts: phone:
dr inż. Krzysztof Świder
+17 865 1548
e-mail: [email protected]
Department: Computer Science and Automatic Control
Type of classes
Semester
Weekly load
2
2
L
Lectures
C
Theoretical
Classes
Lb
Laboratory
15
15
P
Project
Number of
ECTS
credits
6
Course description
Lecture:
Emergence and evolution of knowledge discovery systems. The general schema of knowledge discovery and the
data mining phase. The comparison of existing data analysis methods: i) query and reports, ii) online analytical
processing and iii) data mining. Data warehouses and data marts. Data warehouse design and the star schema.
The key features and operations of OLAP applications. An overview of common data mining techniques
(decition tree models, clusters and association rules). The formal definition of association rules, rule
interestingness and association analysis. The problem of association rule mining and employment of Apriori
algorithm to find frequent itemsets. Data preprocessing for analysis - the motivation and typical methods
(cleaning, integration, transformation and reduction).
Laboratory:
Multidimensional analysis (OLAP) of the real data in MS SQL Server environment. Building classification
models and mining clusters and association rules with Oracle and Matlab tools. Mining multidimensional
association rules from Web data.
Objectives of the course
Learning advanced methods and techniques for knowledge discovery from data. Achieving the practical skills
in using multidimensional analysis and data mining tools to analyse exemplary data.
Examination method
The positive result of written test.
Bibliography
1.
2.
Larose D. T.: Odkrywanie wiedzy z danych Wprowadzenie do eksploracji danych. PWN, 2006.
Hand D., Mannila H., Smyth P.: Eksploracja danych. WNT 2005.
3.
4.
Han J., Kamber M.: Data Mining. Concepts and Techniques. Second Edition. Morgan Kaufmann 2006.
Zgłębianie i analiza danych w Microsoft SQL Server 2000. Przewodnik techniczny. APN PROMISE,
2002.
Lecturer signature
Head of Department signature
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