Multi-Assignment Clustering for Boolean Data


Conventional clustering methods typically
assume that each data item belongs to a single
cluster. This assumption does not hold in
general. In order to overcome this limitation,
we propose a generative method for clustering
vectorial data, where each object can be
assigned to multiple clusters. Using a deterministic
annealing scheme, our method decomposes
the observed data into the contributions
of individual clusters and infers their
parameters.
Experiments on synthetic Boolean data show
that our method achieves higher accuracy
in the source parameter estimation and superior
cluster stability compared to stateof-
the-art approaches. We also apply our
method to an important problem in computer
security known as role mining. Experiments
on real-world access control data show performance
gains in generalization to new employees
against other multi-assignment methods.
In challenging situations with high noise
levels, our approach maintains its good performance,
while alternative state-of-the-art
techniques lack robustness.