Project Details
Projekt
A3: Scalability of Geometric Clustering Algorithms
Applicant
Professor Dr. Christian Sohler
Subject Area
Theoretical Computer Science
Term
since 2022
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 459420781
Due to vast amounts of sea surface height data and very large geographical data sets, there is an increasing need for highly scalable clustering methods that deal with geometric data representations.Therefore, main objective of this project within the RU is to develop and analyze new algorithmic concepts to improve scalability of geometric clustering algorithms with provable guarantees. In order to achieve this goal, we will develop new concepts of dimension reduction for geometrically represented objects such as polygonal curves and corresponding distance measures such as the Fr\'echet distance. For center-based clustering methods with complex geometric centers such as subspace clustering, we will develop new sampling approaches to reduce the number of input points and for clustering of geometric objects we will develop new data reduction methods that combine simplification, dimension reduction and sampling methods to improve scalability of algorithms.We will implement and engineer the most promising approaches and provide them as new tools to projects C1 and C2 to study problems in the context of map aggregation andsea level height analysis, representation and reconstruction.
DFG Programme
Research Units
