Data Mining
Objectives:
The course presents methods for mining and analyzing data. Emphasis is given on web mining. The course also focuses on the application of the presented mining techniques in real problems with the use of appropriate tools.
Skills:
Students will acquire knowledge and practical experience on issues around data analysis through the use of tools that support knowledge discovery algorithms from data.
Prerequisites:
-
Content:
Introduction to knowledge discovery in databases (KDD), statistical methods, classification, association rules, frequent itemsets, clustering.
Case studies of applying and using mining techniques in environments of both commercial and open source tools that support the process of knowledge discovery in databases (e.g. Oracle Data Miner, RapidMiner, WEKA).
Recent research developments in the field such as time series mining and text mining.
Particular emphasis on mining knowledge from the web (web mining): techniques such opinion mining, link analysis and recommendation systems.
Textbooks:
P.-N. Tan, M. Steinbach and V. Kumar, “Introduction to Data Mining” Addison Wesley, 2006.
M. H. Dunham, “Data Mining: Introductory And Advanced Topics”. Pearson Education, 2006.
B. Liu, “Web Data Mining – Exploring Hyperlinks, Contents, and Usage Data”, Second Edition, Springer,` 2011.
A. Rajaraman, J. Leskovec, J. D. Ullman, “Mining of Massive Datasets”, Cambridge University Press, 2010.
Assessment:
Four (4) Homework Assignments (50%)
Final Written Examination (50%)
