Data Mining

Couse Code: Ν2-7014
Weekly Duty: 4 (2Th + 2E)
ECTS: 5
Typical Semester: 7th
Course Category: Specialty Course
Prerequisites:  
Learning Outcomes:
Upon successful completion of the course students should have acquired:

  • basic knowledge of principles, processes and applications of Data Mining
  • understanding of basic data mining algorithms and skills of implementing the algorithms in question
  • ability to apply appropriate data mining techniques to specific problems.
Course Content
  • Introduction – Data mining definition
  • Knowledge extraction techniques
  • Data pre-processing
  • Categorization
    • Basic concepts and algorithms
    • Alternative techniques
  • Clustering
    • Hierarchical algorithms
    • Partitional algorithms
    • Clustering with non-numeric data
  • Association rules – Basic concepts and algorithms
  • Visualization techniques
  • Practical applications
Literature
  1. P.N. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining, Addison Wesley, 2006
  2. A. Rajaraman, J.D. Ullman, Mining of Massive Datasets, Cambridge University Press, 2012
  3. M. H. Dunham, Data Mining: Introductory and Advanced Topics, Prentice Hall, 2003
  4. J. Han, M. Kamber, J. Pei, Data Mining : Concepts and Techniques (3rd edition), Morgan Kaufmann, 2011.
  5. M. Kantardzic, Data Mining: Concepts, Models, Methods, and Algorithms, Wiley-IEEE Press, 2002.
  6. I.H. Witten, E. Frank, M.A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, (3rd edition), Morgan Kaufmann, 2011
  7. Μ. Βαζυργιάννης και Μ. Χαλκίδη. Εξόρυξη Γνώσης από Βάσεις Δεδομένων, και τον Παγκόσμιο Ιστό, Τυπωθήτω – Γιώργος Δαρδανός, 2005
  8. Α. Νανόπουλος, Ι. Μανωλόπουλος, Εισαγωγή στην Εξόρυξη και τις Αποθήκες Δεδομένων, Εκδόσεις Νέων Τεχνολογιών, 2008
Internationalisation I18n