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Conference Papers Year : 2012

Graph-Based Relational Learning with a Polynomial Time Projection Algorithm

Abstract

The paper presents a new projection operator for graphs, named AC- projection, which exhibits good complexity properties as opposed to the graph isomorphism (Θ-subsumption) operator typically used in graph mining. We study the size of the search space and some practical properties of the projection operator. These properties give us a specialization algorithm using simple local operations. Then we prove ex- perimentally that we can achieve an important performance gain (poly- nomial complexity projection) without or with non-significant loss of discovered patterns quality.
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Dates and versions

lirmm-00757471 , version 1 (27-11-2012)

Identifiers

  • HAL Id : lirmm-00757471 , version 1

Cite

Brahim Douar, Michel Liquière, Chiraz Latiri, Yahya Slimani. Graph-Based Relational Learning with a Polynomial Time Projection Algorithm. ILP: Inductive Logic Programming, Jul 2011, Cumberland Lodge, United Kingdom. pp.98-112. ⟨lirmm-00757471⟩
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