Data Warehouses and Data Mining

Learning Outcomes

The students upon the successful completion of the course will be able:

• to evaluate the quality of the data to be analyzed and apply the appropriate data pre-processing techniques,
• to select the appropriate data mining technique based on requirements and data type,
• to design and develop data warehouses,
• to use the appropriate data mining techniques and tools to extract knowledge from data collections,
• to evaluate the quality of data mining results.

Course Contents

• Introduction to the fundamental data mining concepts and techniques: main steps of knowledge and data discovery, requirements of developing data mining approaches.
• Data pre-processing: data cleaning, transformation, dimensionality reduction.
• Data warehouses: multidimensional models, architecture, implementation of data warehouses, OLAP.
• Clustering: partitional, hierarchical, density-based, grid-based, spectral clustering, clustering applications.
• Classification: Bayesian classifiers, decision trees, k-nearest neighbors.
• Association rules: Apriori, representative association rules.
• Quality assessment in data mining: evaluation of classification models, association rules interestingness measures, cluster validity.
• Web mining: link analysis, text mining, web search, PageRank.

Recommended Readings

• Han J. & Kamber M. (2006): Data Mining: Concepts and Techniques, 2nd Edition, Morgan Kaufmann.
• Chakrabarti S. (2002): Mining the Web, Discovering Knowledge from Hypertext Data, Morgan Kaufman Publishers.