A database perspective on knowledge discovery
Communications of the ACM
Automatic Program Construction Techniques
Automatic Program Construction Techniques
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Constraint-Based Local Search
Constraint-Based Mining and Inductive Databases: European Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March 11-13, ... / Lecture Notes in Artificial Intelligence)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
The Need for Open Source Software in Machine Learning
The Journal of Machine Learning Research
Constraint programming for itemset mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards adaptive programming: integrating reinforcement learning into a programming language
Proceedings of the 23rd ACM SIGPLAN conference on Object-oriented programming systems languages and applications
Correlated itemset mining in ROC space: a constraint programming approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic inductive logic programming
Probabilistic inductive logic programming
Declarative modeling for machine learning and data mining
ICFCA'12 Proceedings of the 10th international conference on Formal Concept Analysis
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Today there is only little support for developing software that incorporates a machine learning or a data mining component. To alleviate this situation, we propose to develop programming languages for machine learning and data mining. We also argue that such languages should be declarative and should be based on constraint programming modeling principles. In this way, one could declaratively specify the problem of machine learning or data mining problem of interest in a high-level modeling language and then translate it into a constraint satisfaction or optimization problem, which could then be solved using particular solvers. These ideas are illustrated on problems of constraint-based itemset and pattern set mining.