SPP 1307 - Developing a practical theory for clustering algorithms through data-driven modeling and analysis

Cluster analysis, or simply clustering, is the partitioning of a set of objects into subsets of similar objects. Clustering algorithms have applications in data compression, pattern recognition, biology, network analysis, stochastics, text classification, and machine learning, to name a few. The different applications also determine what is meant by similar objects. On the one hand, there exist many different clustering algorithms successfully used in practice. On the other hand, there are also a large number of theoretical results from the field of theoretical computer science on clustering. However, almost all of the algorithms used in practice can only be analyzed insufficiently and the algorithms originating from theory are not efficient enough for practice. In this project, we will try to close this gap between theory and practice by developing a practice-oriented theory for clustering algorithms. The focus will be on a modeling and the algorithm analysis based on it, which takes into account the specifics of inputs by appropriate parameterizations.

DFG-Procedures Priority Programs