DATA BASES AND KNOWLEDGE MINING
| Title | ΒΑΣΕΙΣ ΔΕΔΟΜΕΝΩΝ ΚΑΙ ΕΞΟΡΥΞΗ ΓΝΩΣΗΣ / DATA BASES AND KNOWLEDGE MINING |
| Code | ΔΜ1029 |
| Interdepartmental Programme | PPS Advanced Computer and Communication Systems |
| Collaborating Schools | Electrical and Computer Engineering Medicine Music Studies Journalism and Mass Communications |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter |
| Coordinator | Andreas Symeonidis |
| Common | No |
| Status | Active |
| Course ID | 600004442 |
Programme of Study: PPS Advanced Computer and Communication Systems
Registered students: 21
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| Eyfyī systīmata-Methodologíes ypologistikīs noīmosýnīs kai efarmogés | Compulsory Course | 1 | 1 | 5 |
| Diktyakī Ypologistikī- Īlektronikó Ebório | Compulsory Course | 1 | 1 | 5 |
| Academic Year | 2017 – 2018 |
| Class Period | Winter |
| Faculty Instructors |
|
| Instructors from Other Categories |
|
| Weekly Hours | 24 |
| Class ID | 600112839
|
Course Type 2016-2020
- Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600112839
- Other: https://alexander.ee.auth.gr:8443/eMASTER
Language of Instruction
- Greek (Instruction, Examination)
- English (Instruction, Examination)
Learning Outcomes
To introduce the students to the advanced database and data mining technologies and demonstrate their use in discovering knowledge and data related with various application domains.
By the end of the semester the students are expected to learn:
a) methodologies for the design and development of a relational database, b) the basic primitives on state-of-the-art database topics, c) the basic primitives of the knowledge discovery in databases process, and d) how to identify the most suitable data mining technique, depending on the problem encountered and the data provided. Focus will be given on classification and clustering algorithms, regression and outlier analysis. The Waikato Environment for Knowledge Analysis (WEKA) data mining suite will be demonstrated, as well as the R framework for statistical computing.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Make decisions
- Work in teams
- Advance free, creative and causative thinking
Course Content (Syllabus)
This module begins with a review of database management systems, discusses advanced database topics and continues with an elaboration on the process of knowledge discovery with emphasis on specific data mining techniques and algorithms.
Indicative lecture titles:
Database Management systems:
- Review of Database Management Systems
- Database Logical Models - Relational Model
- Review of SQL: Design and Development of a Database
- Databases on the Web - Introduction to XML
- Database architectures - Distributed databases
Data Mining:
- Introduction to Data Mining:
Definitions – Examples – Application areas
- Data Exploration
- Data Preparation and Preprocessing
- Data mining techniques (Part I): Classification
Overview – Definitions – Algorithms
- Data mining techniques (Part II): Clustering
Overview – Definitions – Algorithms
- Advanced Data Mining Topics
Educational Material Types
- Notes
- Slide presentations
- Interactive excersises
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
Description
e-THMMY, a blackboard-like system has been developed by the ECE department and is customized to the needs of the ECE courses. e-THMMY allows instructors to post anouncements, communicate with students, upload lectures, exercises and their solutions, set up and run course projects, while it also offers self-assessment capabilities. e-THMMY also supports a Forum for coursework discussion.
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | ✓ | ||||
| Laboratory Work | ✓ | ✓ | |||
| Project | ✓ | ✓ | |||
| Total |
Student Assessment
Description
Project 1: 30%
Project 2: 30%
Exams: 40%
Student Assessment methods
- Written Assignment (Summative)
- Oral Exams (Formative)
- Written Exam with Problem Solving (Formative)
- Report (Summative)
Bibliography
Additional bibliography for study
Βάσεις Δεδομένων
1. Συστήματα Βάσεων Δεδομένων, (4η έκδοση, μεταφρασμένο στα ελληνικά), A. Silberschatz, H. Korth, and S. Sudarshan, Εκδόσεις Γκιούρδας, Έτος έκδοσης 2005.
2. Θεμελιώδεις Αρχές Συστημάτων Βάσεων Δεδομένων, (Τόμος Α & Β, 5η έκδοση, μεταφρασμένο στα ελληνικά),Ramez Elmasri and Shamkant B. Navathe, Εκδόσεις Δίαυλος, Έτος Έκδοσης 2007.
3. Συστήματα Βάσεων Δεδομένων: Θεωρία και Πρακτική Εφαρμογή, Ι. Μανωλόπουλος και Α. Ν. Παπαδόπουλος, Εκδόσεις Νέων Τεχνολογιών, Έτος έκδοσης 2006, Σελ. 556
Εξόρυξη Γνώσης
1. Introduction to Data Mining, P. Tan, M. Steinbach & V. Kumar, 2005, Addison Wesley.
2. Data Mining: Concepts and Techniques (2nd edition), J. Han and M. Kamber, 2006, Morgan Kaufmann.
3. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (2nd edition), H. Ian Witten and F. Eibe, 2005, Morgan Kaufmann.
Last Update
19-05-2014
