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Regular version of the site

Data Mining

2023/2024
Academic Year
ENG
Instruction in English
3
ECTS credits
Course type:
Compulsory course
When:
1 year, 3 module

Instructor

Course Syllabus

Abstract

Covers topics in data mining, including visualization techniques, elements of machine learning theory, classification and regression trees, Generalized Linear Models, Spline approach, and other related topics.
Learning Objectives

Learning Objectives

  • The course gives students an important foundation to develop and conduct their own research as well as to evaluate research of others.
Expected Learning Outcomes

Expected Learning Outcomes

  • Be able to compare mining diverse patterns, including methods for mining multi-level, multi-dimensional patterns, qualitative patterns,
  • Be able to compare negative correlations, compressed and redundancy-aware top-k patterns, and mining long (colossal) patterns.
  • Be able to compare pattern evaluation issues, especially several popularly used measures, such as lift, chisquare, cosine, Jaccard, and Kulczynski, and their comparative strengths.
  • Be able to recall important pattern discovery concepts, methods, and applications, in particular, the basic concepts of pattern discovery, such as frequent pattern, closed pattern, max-pattern, and association rules.
  • Know constraint-based pattern mining, including methods for pushing different kinds of constraints, such as data and pattern-based constraints, anti-monotone, monotone, succinct, convertible, and multiple constraints.
  • Know efficient pattern mining methods, such as Apriori, ECLAT, and FPgrowth.
  • Know various pattern mining applications, such as mining spatiotemporal and trajectory patterns and mining quality phrases.
  • Know well-known sequential pattern mining methods, including methods for mining sequential patterns, such as GSP, SPADE, PrefixSpan, and CloSpan
Course Contents

Course Contents

  • 1. Visualizations (and Getting to Know Orange)
  • 2. Introduction to predictive modelling
  • 3. Model Perfomance
  • 4. Linear models for classification
  • 5. Another models for classification
  • 6. Regularization
  • 7. Clustering
  • 8. Text Mining
  • 9. Projections
  • 10. Embeddings
Assessment Elements

Assessment Elements

  • blocking HW 1
  • blocking HW 2
  • blocking HW 3
Interim Assessment

Interim Assessment

  • 2023/2024 3rd module
    0.33 * HW 1 + 0.33 * HW 2 + 0.34 * HW 3
Bibliography

Bibliography

Recommended Core Bibliography

  • ElAtia, S., Ipperciel, D., & Zaiane, O. R. (2017). Data Mining and Learning Analytics : Applications in Educational Research. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1351385
  • Han, J., & Kamber, M. (2011). Data Mining: Concepts and Techniques (Vol. 3rd ed). Burlington, MA: Morgan Kaufmann. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=377411
  • Larose, D. T., & Larose, C. D. (2015). Data Mining and Predictive Analytics. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=958471
  • S. K. Mourya, & Shalu Gupta. (2013). Data Mining and Data Warehousing. [N.p.]: Alpha Science Internation Limited. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1688519

Recommended Additional Bibliography

  • Brown, M. S. (2014). Data Mining For Dummies. Hoboken: For Dummies. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=842663
  • Knobbe, A. J. (2006). Multi-relational Data Mining. Amsterdam: IOS Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=176061
  • Motoda, H. (2002). Active Mining : New Directions of Data Mining. Amsterdam: IOS Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=87558