Here you can find projects developed at the Algorithms and Data Structures Group of the Heinrich Heine University Düsseldorf.
If you want to try out some of the algorithms on small data (such as the fair K-Means clustering or the coreset clustering), you can use our Clustering Toolkit: https://clustering-toolkit.algo.hhu.de/. You can either upload your own data set or use a data set from the UCI Machine Learning Repository.
kMSR provides a selection of algorithms to solve the k-Min-Sum-Radii problem.
C++
Fair K-Means produces a fair clustering assignment according to the fairness definition of Chierichetti et al. Each point has a binary color, and the goal is to assign the points to clusters such t…
C++
Produces optimal k-means solutions for small instances and small k.
Python
kMSR provides a selection of algorithms to solve the k-Min-Sum-Radii problem.
Fair K-Means produces a fair clustering assignment according to the fairness definition of Chierichetti et al. Each point has a binary color, and the goal is to assign the points to clusters such that the number of points with different colors in each cluster is the same and the cost of the clusters is minimized.
Ergebnisse der Bachelorarbeit "Evaluation von ChatGPT-implementierten Maximalfluss-Algorithmen" von Joschua Theyhsen
