01341nas a2200169   4500008004100000245006100041210006000102260003800162300001400200520081200214100002001026700001501046700001901061700001701080700002301097856005101120       2016                        eng d00aPartition-Based Clustering Using Constraint Optimization0 aPartitionBased Clustering Using Constraint Optimization  bSpringer International Publishing  a282–2993 aPartition-based clustering is the task of partitioning a dataset in a number of groups of examples, such that examples in each group are similar to each other. Many criteria for what constitutes a good clustering have been identified in the literature; furthermore, the use of additional constraints to find more useful clusterings has been proposed. In this chapter, it will be shown that most of these clustering tasks can be formalized using optimization criteria and constraints. We demonstrate how a range of clustering tasks can be modelled in generic constraint programming languages with these constraints and optimization criteria. Using the constraint-based modeling approach we also relate the DBSCAN method for density-based clustering to the label propagation technique for community discovery.1 aGrossi, Valerio1 aGuns, Tias1 aMonreale, Anna1 aNanni, Mirco1 aNijssen, Siegfried  uhttp://dx.doi.org/10.1007/978-3-319-50137-6_11