Abstract
In many settings just finding a good clustering is insufficient and
an explanation of the clustering is required. If the features used
to perform the clustering are interpretable then methods such as
conceptual clustering can be used. However, in many applications
this is not the case particularly for image, graph and other complex
data. Here we explore the setting where a set of interpretable
discrete tags for each instance is available. We formulate the
descriptive clustering problem as a bi-objective optimization
to simultaneously find compact clusters using the features and to
describe them using the tags. We present our formulation in a
declarative platform and show it can be integrated into a standard
iterative algorithm to find all Pareto optimal solutions to the two
objectives. Preliminary results demonstrate the utility of our
approach on real data sets for images and electronic health care
records and that it outperforms single objective and multi-view clustering
baselines.
Keywords
- Constraints and SAT: Modeling;Formulation
- Constraints and SAT: Constraints and Data Mining ; Machine Learning
- Machine Learning: Clustering
