Data Warehouses and Data Mining

Professors Maria Halkidi
Course category CSM/DM
Course ID DS-506
Credits 5
Lecture hours 3 hours
Lab hours 2 hours
Digital resources View on Evdoxos (Open e-Class)

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.