35256 - Data Mining Processes and Techniques M

Learning outcomes

Introduction of the main problems related to data analysis, in order to discover hidden relationships and information useful for strategic decisions. The entire process of discovery knowledge is discussed, including object definition, main techniques of data preparation and data mining algorithms

Course contents

Process of knowledge discovery

• definition of objectives
• selection of data sources
• filtering, reconciliation and data transformation
• data mining
• validation and presentation of the results

Data Mining techniques

• classification with decision trees, neural networks and other algorithms
• association rules
• clustering/segmentation

Analysis of case studies
Examples of use of commercial data mining systems
Architectures of systems with data mining components
Standardization of data mining activities with PMML

Readings/Bibliography

Tan, Steinbach, Kumar, "Introduction to Data Mining", Addison-Wesley, 2005. ISBN : 0321321367

Teaching methods

Main activities are in class. Case studies are also proposed for the solution with open-source software.

Assessment methods

Oral examination; students can also present their own projects, previously agreed with the teacher.

Teaching tools

Notes provided by the teacher. Laboratory activity with open-source tools

Links to further information

http://www-db.deis.unibo.it/~csartori/didattica/03data_mining/00data_mining.html

Office hours

See the website of Claudio Sartori