Discrete Applied Mathematics - Special volume on computational molecular biology DAM-CMB series volume 2
A Linear Time Algorithm for Deciding Interval Graph Isomorphism
Journal of the ACM (JACM)
Polynomial Time Inference of Extended Regular Pattern Languages
Proceedings of RIMS Symposium on Software Science and Engineering
Ordered term tree languages which are polynomial time inductively inferable from positive data
Theoretical Computer Science - Algorithmic learning theory(ALT 2002)
Frequent subgraph mining in outerplanar graphs
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Polynomial time inductive inference of TTSP graph languages from positive data
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Exact Learning of Finite Unions of Graph Patterns from Queries
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
Learning Block-Preserving Outerplanar Graph Patterns and Its Application to Data Mining
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Mining of frequent block preserving outerplanar graph structured patterns
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
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A graph is an interval graph if and only if each vertex in the graph can be associated with an interval on the real line such that any two vertices are adjacent in the graph exactly when the corresponding intervals have a nonempty intersection. A number of interesting applications for interval graphs have been found in the literature. In order to find structural features common to structural data which can be represented by intervals, this paper proposes new interval graph structured patterns, called linear interval graph patterns, and a polynomial time algorithm for finding a minimally generalized linear interval graph pattern explaining a given finite set of interval graphs.