TY - CHAP
T1 - Partition-Based Clustering Using Constraint Optimization
T2 - Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach
Y1 - 2016
A1 - Valerio Grossi
A1 - Tias Guns
A1 - Anna Monreale
A1 - Mirco Nanni
A1 - Siegfried Nijssen
AB - Partition-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.
JF - Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach
PB - Springer International Publishing
UR - http://dx.doi.org/10.1007/978-3-319-50137-6_11
ER -