73020 - Data Mining Processes And Techniques M

Learning outcomes

The course aims to provide the students with the knowledge and skills necessary for the analysis of data in order to discover relationships and useful information for decisions support. Particular attention is devoted to the presentation of the discovery process from the definition of the objectives and algorithms processing. 

The second module provides a demonstration of how traditional data mining techniques can be profitably applied for the efficient management of multimedia collections in term of localization of data of interest and for purposes of visualization and browsing.

Course contents

MODULE 1 (Data Mining)

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

Processes and systems

• Analysis of case studies
  
• Examples with commercial data mining systems
  
• Architectures of systems with data mining components
  
• Standards for data mining components: PMML.
  

 

MODULE 2 (Multimedia Data Mining)

Multimedia data and content representations

• MM data and applications
• MM data coding

• MM data content representation

Automatic techniques for MM data semantic annotations

How to find MM data of interest

• Description models for complex MM objects
• Similarity measures for MM data content

• MM Data Base Management Systems

Efficient algorithms for MM data retrieval

• MM query formulation paradigms
• Sequential retrieval of MM data
• Index-based retrieval of MM data

Browsing MM data collections

MM data presentation

• User interfaces
• Visualization paradigms

• Dimensionality reduction techniques

Result accuracy, use cases and real applications

• Quality of the results and relevance feedback techniques
• Use cases
 and demos of some applications

 

Readings/Bibliography

Education material provided by the teachers (copies of the slides used in the classroom, scientific literature).

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

Teaching methods

Most course lectures are in "traditional" classrooms and exploit the slides. Case studies are also proposed based on open-source software.

The students can directly arrange with each teacher a Project Activity of Data Mining Techniques and Processing based on their own preferences on provided topics.

Assessment methods

The exam evaluation consists of an oral examination. To participate to the lab programming exam, interested students have to register themselves by exploiting the usual UniBO Web application, called AlmaEsami.

Teaching tools

In traditional classrooms, the course lectures will make extensive usage of slides.
Laboratory activity with open-source tools.

Links to further information

http://www-db.deis.unibo.it/courses/DM

Office hours

See the website of Claudio Sartori

See the website of Ilaria Bartolini