ALMA MATER STUDIORUM - UNIVERSITÀ DI BOLOGNA
Bologna, September 23rd, 2015
Data Mining M Laurea Magistrale in Ingegneria Informatica
University of Bologna
Course presentation Academic Year 2015/2016
Home page: http://www-db.disi.unibo.it/courses/DM/
Electronic version: 0.01.Presentation.pdf
Electronic version: 0.01.Presentation-2p.pdf
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Prof. Claudio Sartori e Prof.ssa Ilaria Bartolini Department of Computer Science and Engineering (DISI)
Viale Risorgimento, 2 - 40136, Bologna
Contacts
E-mail: {claudio.sartori,i.bartolini}@unibo.it
Telephone:
051 20 93554 (Sartori) - 051 20 93550 (Bartolini)
Web:
http://www-db.disi.unibo.it/csartori/
http://www-db.disi.unibo.it/ibartolini/
Office hours:
Refer to personal Web page, c/o Palazziana DISI, close to
entrance Via Vallescura (Prof. Sartori)
on Fridays, from 16:00 to 18:00, c/o Palazziana DISI,
close to entrance Via Vallescura (Prof.ssa Bartolini)
Teachers
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Name: “Data Mining M”
Credits: 8
Teaching hours: 64 hours
Period: Semester I
September 21st 2015 – December 18th 2015
Course organization: divided into two learning modules
Module I – Data Mining (Prof. Sartori)
From November 5th till December 17th 2015
Module II – Multimedia Data Mining (Prof.ssa Bartolini)
Form September 23rd till November 4th 2015
General information
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Teaching hours:
Wednesday – 14:00-17:00 – Room 7.8
Scuole Sirani - Via Saragozza, 8
Thursday – 9:00-11:00 – Room 5.4
Course calendar
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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
Parts of the course
Process of knowledge discovery
Data Mining techniques
Multimedia data content representation
Efficient and effective techniques for multimedia data retrieval,
browsing, and visualization
Course contents
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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 with commercial data mining systems
Architectures of systems with data mining components
Standards for data mining components: PMML
Program – Module I (Data Mining)
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Multimedia data and content representations
MM data and applications
MM data coding
MM data content representation
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 information retrieval
MM query formulation paradigms
Sequential retrieval of MM data
Index-based retrieval of MM data
Automatic techniques for MM data semantic annotations
Browsing MM data collections
MM data presentation
Result accuracy, use cases and real applications
Program – Module II (Multimedia Data Mining)
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Course home page
Contents:
News
Copy of slides in
PDF format
Bibliography
Software tools and
useful links
Assessment
methods
Exam sessions
Topics for project
activities
http://www-db.disi.unibo.it/courses/DM/
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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
Zhang, Zhang, "Multimedia Data Mining: A Systematic Introduction
to Concepts and Theory", Chapman and Hall/CRC, 2008. ISBN:
9781584889663
Readings/Bibliography
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Most course lectures are in "traditional" classrooms and exploit the
slides
Case studies are also proposed based on open-source software and
frameworks
Teaching methods
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The exam evaluation consists of an oral examination
To participate to the exam, interested students have to register
themselves by exploiting the usual UniBO Web application, called
AlmaEsami
The students can directly arrange with each teacher a
Project Activity of Data Mining M based on their own preferences on
provided topics (see Web page for more details)
Assessment methods
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Six examination sessions per year divided as follows:
three sessions during the winter
starting from June, at the request of the students
Examination sessions
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