@article {955,
	title = {The Inductive Constraint Programming Loop},
	journal = {IEEE Intelligent Systems},
	year = {2017},
	abstract = {Constraint programming is used for a variety of real-world optimization problems, such as planning, scheduling and resource allocation problems. At the same time, one continuously gathers vast amounts of data about these problems. Current constraint programming software does not exploit such data to update schedules, resources and plans. We propose a new framework, which we call the inductive constraint programming loop. In this approach data is gathered and analyzed systematically in order to dynamically revise and adapt constraints and optimization criteria. Inductive Constraint Programming aims at bridging the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other.},
	doi = {10.1109/MIS.2017.265115706},
	author = {Bessiere, Christian and De Raedt, Luc and Tias Guns and Lars Kotthoff and Mirco Nanni and Siegfried Nijssen and Barry O{\textquoteright}Sullivan and Paparrizou, Anastasia and Dino Pedreschi and Simonis, Helmut}
}
@inbook {877,
	title = {Partition-Based Clustering Using Constraint Optimization},
	booktitle = {Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach},
	year = {2016},
	pages = {282{\textendash}299},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {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.},
	doi = {10.1007/978-3-319-50137-6_11},
	url = {http://dx.doi.org/10.1007/978-3-319-50137-6_11},
	author = {Valerio Grossi and Tias Guns and Anna Monreale and Mirco Nanni and Siegfried Nijssen}
}
