1 research outputs found
Integer Programming Relaxations for Integrated Clustering and Outlier Detection
In this paper we present methods for exemplar based clustering with outlier
selection based on the facility location formulation. Given a distance function
and the number of outliers to be found, the methods automatically determine the
number of clusters and outliers. We formulate the problem as an integer program
to which we present relaxations that allow for solutions that scale to large
data sets. The advantages of combining clustering and outlier selection
include: (i) the resulting clusters tend to be compact and semantically
coherent (ii) the clusters are more robust against data perturbations and (iii)
the outliers are contextualised by the clusters and more interpretable, i.e. it
is easier to distinguish between outliers which are the result of data errors
from those that may be indicative of a new pattern emergent in the data. We
present and contrast three relaxations to the integer program formulation: (i)
a linear programming formulation (LP) (ii) an extension of affinity propagation
to outlier detection (APOC) and (iii) a Lagrangian duality based formulation
(LD). Evaluation on synthetic as well as real data shows the quality and
scalability of these different methods.Comment: 10 pages, 10 figure