Data Mining

ΕΠ19 - Data Mining

General Information

School: Digital Technology

Department: Informatics and Telematics

Level: Undergraduate

Course Title: Data Mining

Course id: ΕΠ19

Type: Core Course 

Semester: 7

Teaching and Examination Language: Greek

Is the course offered in Erasmus: Yes

Course web-page: https://eclass.hua.gr/courses/DIT129/

Activities

Lectures (Theory): 3,0

Lab lectures: 0,0

ECTS credits: 5,0

Learning Outcomes

The course aims to introduce basic data mining concepts, presenting requirements and needs for the application of new methods and techniques for data analysis.  The course extensively presents algorithms for supervised and unsupervised data mining / learning, such as clustering, classification, association rules. The course also presents techniques concerning designing and developing of data warehouses. At the end of the course the students must be able to:
- Analyze a data set and identify useful knowledge
- Categorize the knowledge extraction problem in the types of problems that has been taught
- Use data mining software in a way that creates added value

General Skills

- Independent work
- Team work
- Adaptation in new conditions
- Decision Making
- Promoting free, creative and deductive reasoning

Course Content



Data warehouses. Data analysis. OLAP systems. The knowledge mining process. Data clustering. Classification. Association rules. Temporal mining. Uncertainty handling in data mining tasks. Semi-structured data, information retrieval from the web.

Learning and Teaching Methods - Evaluation

Teaching methods: - Face-to-face (lectures
 & lab)
- Hands-on exercises
- Assignments

Use of ICT: 

- Specific software for data mining
- Group projects that require data processing and problem solving with ICT
- Dissemination and organization of course material using OpenClass
- Communication via OpenClass and emails

Course Organization

 

Activity

Semester work load

Lectures

27,0

Lab exercises

12,0

Individual of group projects

20,0

Lab report preparation

20,0

Thesis 

  

Independent Study

46,0

Total

125

Assessment

 

Literature

- Μ. Halkidi, Μ. Vazirgiannis (2005). Data mining from databases and the WWW (in greek).
- Dunham, Margaret H (2004). Data Mining.
- Principles of Data Mining , David Hand, Heikki Mannila, and Padhraic Smyth, MIT Press, August 2001.
- Soumen Chakrabarti. "Mining the Web, Discovering Knowledge from Hypertext Data", Morgan Kaufman Publishers, ISBN 1-55860-754-4.
- J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2nd ed., 2006
- Trevor Hastie, Robert Tibshirani, Jerome Friedman. "The Elements of Statistical Learning Theory, Data Mining, Inference and Prediction", Springer Verlag, 2003.
- Tom Mitchell. "Machine Learning", McGraw Hill, 1997

 

Journals
- ACM Transactions on Intelligent Systems and Technology (TIST)
- Data Mining and Knowledge Discovery
- Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
- Knowledge and Information Systems (KAIS)
- Social Network Analysis and Mining
- ACM Transactions on Knowledge Discovery from Data (TKDD)

Conferences
- ACM Conference on Recommender Systems
- SIAM International Conference on Data Mining (SDM)
- IEEE International Conference on Data Mining (ICDM)
- European Conference on Machine Learning and Knowledge Discovery in Databases (PKDD)