Topic outline
General
Learn how to mine your own data
Today’s world generates more data than ever before! Being able to turn it into useful information is a key skill. This short course introduces you to basic theoretical, as well as, practical data mining using the WEKA workbench. We’ll dispel the mystery that surrounds the subject. We’ll briefly explain the theoretical principles of some popular algorithms. We’ll show you how to use them on selected sample datasets. You’ll get some experience actually mining data during this course, and afterwards you’ll be equipped to mine your own.
WEKA originated at the University of Waikato in New Zealand. Its founder, prof. Ian H. Witten has authored a leading book on data mining. In this course, you'll be using WEKA. More detailed information on presented algorithms and methods can be found in the book (see the Additional Readings section of this course).
Have fun!
Teacher of this course
assist. prof. Branko Kavšek
e-mail: branko.kavsek@upr.si
web: https://www.famnit.upr.si/sl/zaposleni-in-sodelavci/branax/
Additional Readings
Lecture slides
The WEKA workbench
You can get WEKA for free from https://www.cs.waikato.ac.nz/ml/weka/ (under the download section). You may find other related resources on this webpage.
Data repositories
- The UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/index.php,
- The Kaggle ML Repository: https://www.kaggle.com/,
- The FigShare Repository: https://figshare.com/.
Acknowledgement
The teachers of this course gratefully acknowledge the European Commission for funding the InnoRenew CoE project (Grant Agreement #739574) under the Horizon 2020 Widespread-Teaming program and the Republic of Slovenia (Investment funding of the Republic of Slovenia and the European Union of the European Regional Development Fund). They also acknowledge the Slovenian Research Agency ARRS for funding the project J2-2504.