Learning as optimization: stochastic generation of multiple knowledge
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
String editing and longest common subsequences
Handbook of formal languages, vol. 2
Multiple Comparisons in Induction Algorithms
Machine Learning
Machine Learning
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Data Mining and Knowledge Discovery
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
Complete Mining of Frequent Patterns from Graphs: Mining Graph Data
Machine Learning
Scalable Feature Mining for Sequential Data
IEEE Intelligent Systems
Refinement of Rule Sets with JoJo
ECML '93 Proceedings of the European Conference on Machine Learning
A Logical Generalization of Formal Concept Analysis
ICCS '00 Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues
Formalizing Hypotheses with Concepts
ICCS '00 Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues
Optimized Substructure Discovery for Semi-structured Data
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
A Framework for Developing Embeddable Customized Logics
LOPSTR '01 Selected papers from the 11th International Workshop on Logic Based Program Synthesis and Transformation
Generation of Efficient Interprocedural Analyzers with PAG
SAS '95 Proceedings of the Second International Symposium on Static Analysis
Longest Common Subsequence from Fragments via Sparse Dynamic Programming
ESA '98 Proceedings of the 6th Annual European Symposium on Algorithms
Mining Association Rules in Multiple Relations
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Refinement Operators Can Be (Weakly) Perfect
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
A Refinement Operator for Theories
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Ilp: a short look back and a longer look forward
The Journal of Machine Learning Research
Introduction to logical information systems
Information Processing and Management: an International Journal
Classifier construction by graph-based induction for graph-structured data
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
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Many application domains make use of specific data structures such as sequences and graphs to represent knowledge. These data structures are ill-fitted to the standard representations used in machine learning and data-mining algorithms: propositional representations are not expressive enough, and first order ones are not efficient enough. In order to efficiently represent and reason on these data structures, and the complex patterns that are related to them, we use domain-specific logics. We show these logics can be built by the composition of logical components that model elementary data structures. The standard strategies of top-down and bottom-up search are ill-suited to some of these logics, and lack flexibility. We therefore introduce a dichotomic search strategy, that is analogous to a dichotomic search in an ordered array. We prove this provides more flexibility in the search, while retaining completeness and non-redundancy. We present a novel algorithm for learning using domain specific logics and dichotomic search, and analyse its complexity. We also describe two applications which illustrates the search for motifs in sequences; where these motifs have arbitrary length and length-constrained gaps. In the first application sequences represent the trains of the East-West challenge; in the second application they represent the secondary structure of Yeast proteins for the discrimination of their biological functions.