Data and Text Mining

professor: prof. dr. Nada Lavrač

professor: doc. dr. Petra Kralj Novak

professor: prof. dr. Bojan Cestnik

professor: prof. dr. Dunja Mladenić

Course

• Information and Communication Technologies (ICT2): Data and Text Mining / Podatkovno in tekstovno rudarjenje (20 ECTS)

Scheduled lectures and course materials in school year 2019/2020

The course is divided into three parts:

• data preprocessing (lectured by prof. dr. Bojan Cestnik),
• data mining (lectured by prof. dr. Nada Lavrač and doc. dr. Petra Kralj Novak),
• text mining (lectured by prof. dr. Dunja Mladenić).

Materials:

• Nada Lavrac:
  • Lecture notes from Oct. 21, 2019
  • Lecture notes from Nov. 4 and Nov. 11, 2019
• Petra Kralj Novak:
  
  --- Oct. 23, 2019 ---
  • Lecture notes from Oct. 23, 2019
  
  --- Nov. 6, 2019 ---
  • Lecture notes from Nov. 6, 2019
  • Decision trees (.pdf)
  • Entropy and information gain (.pdf)
  • Hands on: Orange workflow on language bias of decision trees (.ows) and data (.csv)
  • Home assignments: On slides 13 and 20 of the lecture notes
  
  --- Nov. 25, 2019 ---
  • Lecture notes from Nov. 25, 2019
  • Homework assignments
  
  --- Dec. 2, 2019 ---
  • Lecture notes from Dec. 2, 2019, updated on Dec. 19, 2019
  • Naive Bayes
  • Homework assignments
  
  --- Dec. 16, 2019 ---
  • Lecture notes from Dec. 16, 2019, updated on Dec. 19, 2019 - Clustering
  • Homework assignments
  
  --- Jan. 13, 2020 ---
  • Lecture notes from Jan. 13, 2020 - Accosiation rules
  • Association rules
  • Lecture notes from Jan. 13, 2020 - Artificial neural networks

Practice materials: dr. Petra Kralj Novak

• Hands on Scikit-learn and Keras including data gitlab repository

Literature:

1. Bramer, Max: Principles of Data Mining (2007)
2. Liaw, Andy, and Wiener, Matthew: "Classification and regression by randomForest" R news 2.3 (2002): 18-22.
3. Loh, Wei‐Yin: Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1.1 (2011): 14-23.
4. Fawcett, Tom. "An introduction to ROC analysis." Pattern recognition letters 27.8 (2006): 861-874.

Course requirements, data mining part:

• Attending lectures, practical exercises and theory exercises
• Written exam
• Seminar: half a page proposal, 4 pages written report, oral presentation, all in English

1. Written exam
2. Data mining seminar
   • Half a page seminar proposal on written exam day
   • Data analysis of your own data (Orange or scikit-learn recommended)
   • Deliver a 4 pages written report (printed and electronic copy) in Information Society paper format
     on seminar presentations day (use paper template and guidelines)
   • Oral presentation of seminar results (10 minutes for presentation + 5 minutes discussion, use slides template)

Examples of data mining seminars:

• Janez Bucik (.pdf)
  Microsoft stock quotes dependency analysis
• Valentin Koblar (in Slovene .pdf)
  Napoved menjalnega tečaja ameriškega dolarja na podlagi menjalnih tečajev tujih valut

Ideas for seminars

1. Analyze some data where you are the domain expert, use at least two algorithms
2. Find some interesting data to analyze, possible sources:
   • Statistični urad Republike Slovenije
   • Ljubljanska borza d.d.
   • Banka Slovenije
   • World Health Organization
   • Center for Climatic Research, University of Delaware

Templates

• presentation template (.pot)
• paper template (.doc)
• paper guidelines (.doc)

Useful links

• Orange and datasets for practice
• Scikit-learn
• Weka
• ClowdFlows

Link to last year's web page - Data Mining and Knowledge Discovery 2018/2019
Last update: January 27, 2020