ALMA MATER STUDIORUM - UNIVERSITÀ DI BOLOGNA
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
Bologna, September 23rd, 2015
Teachers
 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)
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General information
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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
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Course calendar
 Teaching hours:
 Wednesday – 14:00-17:00 – Room 7.8
 Scuole Sirani - Via Saragozza, 8
 Thursday – 9:00-11:00 – Room 5.4
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Course contents
 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
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Program – Module I (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
 Analysis of case studies
 Examples with commercial data mining systems
 Architectures of systems with data mining components
 Standards for data mining components: PMML
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Program – Module II (Multimedia Data Mining)
 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
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Course home page
http://www-db.disi.unibo.it/courses/DM/
Contents:
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I. Bartolini
News
Copy of slides in
PDF format
Bibliography
Software tools and
useful links
Assessment
methods
Exam sessions
Topics for project
activities
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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
 Zhang, Zhang, "Multimedia Data Mining: A Systematic Introduction
to Concepts and Theory", Chapman and Hall/CRC, 2008. ISBN:
9781584889663
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Teaching methods
 Most course lectures are in "traditional" classrooms and exploit the
slides
 Case studies are also proposed based on open-source software and
frameworks
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Assessment methods
 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)
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Examination sessions
 Six examination sessions per year divided as follows:
 three sessions during the winter
 starting from June, at the request of the students
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