The Parameterized Complexity of Clustering Incomplete Data


  • Eduard Eiben Royal Holloway, University of London
  • Robert Ganian TU Wien
  • Iyad Kanj DePaul University, School of Computing
  • Sebastian Ordyniak University of Leeds
  • Stefan Szeider TU Wien




We study fundamental clustering problems for incomplete data. Specifically, given a set of incomplete d-dimensional vectors (representing rows of a matrix), the goal is to complete the missing vector entries in a way that admits a partitioning of the vectors into at most k clusters with radius or diameter at most r. We give tight characterizations of the parameterized complexity of these problems with respect to the parameters k, r, and the minimum number of rows and columns needed to cover all the missing entries. We show that the considered problems are fixed-parameter tractable when parameterized by the three parameters combined, and that dropping any of the three parameters results in parameterized intractability. A byproduct of our results is that, for the complete data setting, all problems under consideration are fixed-parameter tractable parameterized by k+r.




How to Cite

Eiben, E., Ganian, R., Kanj, I., Ordyniak, S., & Szeider, S. (2021). The Parameterized Complexity of Clustering Incomplete Data. Proceedings of the AAAI Conference on Artificial Intelligence, 35(8), 7296-7304. Retrieved from



AAAI Technical Track on Machine Learning I