@inproceedings{kotthoff_complex_2015,
  title = {Complex {Clustering} {Using} {Constraint} {Programming}: {Modelling} {Electoral} {Map} {Creation}},
  abstract = {Traditional clustering is limited to a single collection of objects, described by a set of features under simple objectives and constraints. Though this setting can scale to huge data sets, many real world problems do not fit it. Consider the problem motivating this work: creating electoral district maps. Not only are two sets of objects (electoral districts and elected officials) separately  clustered simultaneously under complex constraints, the clusters must be matched and it is required to find a global optimum. Existing formulations of clustering such as those using procedural languages or convex programming cannot handle such complex settings. In this paper we explore clustering this complex setting using constraint programming. We implement our methods in the Numberjack language and make use of large-scale solvers such as Gurobi which exploit multi-core architectures.},
  booktitle = {14th {International} {Workshop} on {Constraint} {Modelling} and {Reformulation}},
  author = {Kotthoff, Lars and O'Sullivan, Barry and Ravi, S. S. and Davidson, Ian},
  month = sep,
  year = {2015},
  month_numeric = {9}
}