In this lecture, the students learn how the field of data mining has originated from predictive modeling, the core techniques of unsupervised (clustering) and supervised data mining are introduced (rules, trees, naive bayes, multilayer perceptrons, etc.) and applied in both a classification and a regression setting. Special attention is given to spatial data including relevant algorithms, treatment of missing values, treatment of uncertainty, spatial autocorrelation, model selection, model fusion, and data cleaning.
By completing this module, students will be enabled to extract knowledge from spatial and spatio-temporal datasets following techniques from data mining and machine learning including linear models, kNN models, regression models, classification models, decision trees, NaiveBayes, Support Vector Machines and more. These methods are applied to spatial datasets including point clouds, trajectory datasets, event databases, spatial networks, text, and multimedia data. Students get an overview of methods and techniques to explore big geospatial datasets using data mining techniques.
This conference runs in an inverted classroom manner due to Corona. That is, every week, a detailed list of resources including
and other media is published.
Then, students can attend an interactive walkthrough session as a video conference during the planned lecture timeslot.
The timeslot is currently
Tuesday, 9:00 - 11:15
We appreciate your feedback and support. You can drop us a line at any time. If you have interesting examples, you want to share with your fellow students, you can either send it to me via email or create a pull request on GitHub. I would be happy to include your examples, solutions and portations in the lecture.