TY - JOUR
T1 - The Inductive Constraint Programming Loop
JF - Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach
Y1 - 2017
A1 - Mirco Nanni
A1 - Siegfried Nijssen
A1 - Barry O'Sullivan
A1 - Paparrizou, Anastasia
A1 - Dino Pedreschi
A1 - Simonis, Helmut
AB - 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, that we call the Inductive Constraint Programming (ICON) 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 end.
VL - 10101
UR - https://link.springer.com/content/pdf/10.1007/978-3-319-50137-6.pdf#page=307
ER -
TY - JOUR
T1 - The Inductive Constraint Programming Loop
JF - IEEE Intelligent Systems
Y1 - 2017
A1 - Bessiere, Christian
A1 - De Raedt, Luc
A1 - Tias Guns
A1 - Lars Kotthoff
A1 - Mirco Nanni
A1 - Siegfried Nijssen
A1 - Barry O'Sullivan
A1 - Paparrizou, Anastasia
A1 - Dino Pedreschi
A1 - Simonis, Helmut
AB - 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.
ER -
TY - ABST
T1 - Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach.
Y1 - 2016
A1 - Bessiere, Christian
A1 - De Raedt, Luc
A1 - Lars Kotthoff
A1 - Siegfried Nijssen
A1 - Barry O'Sullivan
A1 - Dino Pedreschi
AB - A successful integration of constraint programming and data mining has the potential to lead to a new ICT paradigm with far reaching implications. It could change the face of data mining and machine learning, as well as constraint programming technology. It would not only allow one to use data mining techniques in constraint programming to identify and update constraints and optimization criteria, but also to employ constraints and criteria in data mining and machine learning in order to discover models compatible with prior knowledge. This book reports on some key results obtained on this integrated and cross- disciplinary approach within the European FP7 FET Open project no. 284715 on “Inductive Constraint Programming” and a number of associated workshops and Dagstuhl seminars. The book is structured in five parts: background; learning to model; learning to solve; constraint programming for data mining; and showcases.
ER -
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 -