Data Mining

ISTDBM2 - 3 ECTS - 4th Edition

Abstract

This course introduces the basics notions of datamining. It surveys techniques for data preprocessing, i.e., cleansing, integration, data reduction. Then, it jumps into foundational aspects of data mining like: Frequent Patterns mining, classification, clustering, outliers detection. The course examination consists of both a project and an exam with open and multiple choice questions and exercises.

SCHEDULE

NB: The course schedule can be subject to changes!

Example Projects

• Students alcohol consumption
• NASA Exoplanet Archive
• Data mining for Star and Beer Review Dataset

Project Checklist

-REGISTER YOUR GROUP

• Identify the domain of choice, e.g., dataset presented above
  • YOU DO NOT HAVE TO PICK ONLY ONE DATASET
  • BUT BE CAREFUL BECAUSE YOU MIGHT HAVE TO CLEAN MORE THAN ONE
  • YOU CAN SUBMIT THE TAKS FOR APPROVAL
• you can pick out of problems (preprocessing + 2)
  • cleansing/preprocessing (mandatory)
  • frequent pattern mining
  • classification
  • clustering
• Explain why why you’re happy with the results.
  • evaluate the associations rules
  • evaluate the classification
  • evaluate the clustering proceedure
• present a notebook with comments and discussion of your result (github)

For visualization

https://streamlit.io/

Suggested Material

• Professor’s Choice: Data Mining: Concepts and Techniques, 3rd ed.
• Slides and Lecture Notes https://wiki.illinois.edu//wiki/display/cs412/2.+Course+Syllabus+and+Schedule
• Exercise (we will do them) http://www.cse.msu.edu/~ptan/dmbook/software/
• https://dataminingbook.info/book_html/
• http://www.mmds.org/#support

Proposed Dataset

• Meme Datasets:
  • Know Your Meme Scraped
  • DBPedia Enrichment
  • Google Vision Dataset
  • Instances from Image Flip
• Industrial ERP (shared by email) (Courtesy of LEM International)
• Hackathon Dataset (Courtesy of Prof Alex Nolte)

Data Is Plural — 2000+ Dataset Archive

Previous Years

• 2023
• 2022