Data Mining M Laurea Magistrale in Ingegneria Informatica - - PDF document

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)

• n Fridays, from 16:00 to 18:00, c/o Palazziana DISI,

close to entrance Via Vallescura (Prof.ssa Bartolini)

Teachers

• I. Bartolini

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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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• I. Bartolini

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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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