@inproceedings{kotthoff_constraint-based_2013,
  title = {Constraint-based {Clustering}},
  abstract = {Machine learning and constraint programming are almost completely independent research fields. However, there are significant opportunities for synergy between them. In this presentation, we introduce a constraint programming approach to the classification problem in machine learning. Specifically, we treat classification as a clustering problem. Previous approaches have used constraints as a means of representing background knowledge to improve the quality of the resulting clustering. We show how to use such constraints to not only guide the machine learning algorithms, but replace them entirely. Our approach uses an off-the-shelf constraint solver to find the clustering that reflects as much background knowledge as possible. A second formulation allows us to optimise for the objectives commonly used in machine learning algorithms, such as maximising the inter-cluster distances. We present an evaluation results of our approaches on a variety of well-known benchmarks covering a range of different application domains. Our approaches can significantly outperform standard clustering methods used in machine learning in terms of the quality of the resulting clustering and classification. In addition, the constraint programming formulation provides much more flexibility and customisation opportunities than standard machine learning approaches.},
  booktitle = {10th {International} {Conference} on {Integration} of {Artificial} {Intelligence} ({AI}) and {Operations} {Research} ({OR}) techniques in {Constraint} {Programming}},
  author = {Kotthoff, Lars and O'Sullivan, Barry},
  month = may,
  year = {2013},
  note = {Presentation-only abstract.},
  month_numeric = {5}
}