For the same purpose, ClosedGraph must examine extensions from all vertices. The goal of Frequent Subgraph Mining (FSM) is to find (ii) cgSpan uses an efficient look-up table to check if early subgraphs in a given labeled graphs set that occur more termination can be applied to the graph. Only a single frequently than a given value. This value, known as support, lookup of the edge projections set of the last DFS code is usually expressed as a percentage of the set size. FSM of the graph is required. After the lookup, the equivalent algorithms can be designed to produce two types of output.

Conceptual Graphs (CGs) are a graph-based knowledge representation formalism. In this paper we propose cgSpan a CG frequent pattern mining algorithm. It extends the DMGM-GSM algorithm that takes taxonomy-based labeled graphs as input; it includes three more kinds of knowledge of the CG formalism: (a) the fixed arity of relation nodes, handling graphs of neighborhoods centered on relations rather than graphs of nodes, (b) the signatures, avoiding patterns with concept types more general than the maximal types specified in signatures and (c) the inference rules, applying them during the pattern mining process. The experimental study highlights that cgSpan is a functional CG Frequent Pattern Mining algorithm and that including CGs specificities results in a faster algorithm with more expressive results and less redundancy with vocabulary.