  Random restart of a given algorithm produces many partitions to yield a
consensus clustering. Ensemble methods such as consensus clustering have been
recognized as more robust approaches for data clustering than single clustering
algorithms. We propose the use of determinantal point processes or DPP for the
random restart of clustering algorithms based on initial sets of center points,
such as k-medoids or k-means. The relation between DPP and kernel-based methods
makes DPPs suitable to describe and quantify similarity between objects. DPPs
favor diversity of the center points within subsets. So, subsets with more
similar points have less chances of being generated than subsets with very
distinct points. The current and most popular sampling technique is sampling
center points uniformly at random. We show through extensive simulations that,
contrary to DPP, this technique fails both to ensure diversity, and to obtain a
good coverage of all data facets. These two properties of DPP are key to make
DPPs achieve good performance with small ensembles. Simulations with artificial
datasets and applications to real datasets show that determinantal consensus
clustering outperform classical algorithms such as k-medoids and k-means
consensus clusterings which are based on uniform random sampling of center
points.
