Lecture: Data Mining I

    About the lecture

    “Data mining, also called knowledge discovery in databases (KDD), is the process of discovering interesting and useful patterns and relationships in large volumes of data. The field combines tools from statistics and artificial intelligence (such as neural networks and machine learning) with database management to analyze large digital collections, known as data sets. Data mining is widely used in business (insurance, banking, retail), science research (astronomy, medicine), and government security (detection of criminals and terrorists).”

    Clifton, Christopher (2010). "Encyclopædia Britannica: Definition of Data Mining".

    Course content

    • Introduction to Data Mining and the KDD process
    • Getting to know the data
    • Associations Rules Mining
    • Supervised learning
    • Unsupervised learning
    • Outlier detection

    Organisation

    • Lecturer: Prof. Dr. Eirini Ntoutsi
    • Teaching assistants: Tai Le Quy || Vasileios Iosifidis || Maximilian Idahl || Wazed Ali || Shaheer Asghar
    • Participation is equivalent to 5 ECTS credits (1 ECTS credit equals 30 hours of study).
    • We will use StudIP for discussions/announcements/material/schedule.
    • Kick-off lecture: Wednesday 10/04/2019

    Schedule

    Check Stud.IP for actual schedule and announcements on the lecture for SoSe19.

    • Lecture 1. "Introduction": Slides ||
    • Lecture 2. "Getting to know the data" Slides ||
    • Lecture 3. "Frequent Itemsets and Association Rules Mining" Slides ||
    • Lecture 4 & 5. "Introduction to Classification & Decision trees" Slides ||
    • Lecture 6. "Evaluation of classifiers & KNN classifiers" Slides ||
    • Lecture 7. "Bayesian Classifiers" Slides ||
    • Lecture 8. "Support Vector Machines" Slides ||
    • Lecture 9. "Introduction to Clustering and k-Means"
    • Lecture 10. "k-Medoids & Clustering evaluation"
    • Lecture 11. "Density-based clustering & Grid-based clustering"
    • Lecture 12. "Hierarchical clustering"
    • Lecture 13. "Outlier detection"
    • Lecture 14. "Closing lecture & Poster project presentations"