FGMAC: Frequent subgraph mining with Arc Consistency

Douar, Brahim; Liquière, Michel; Latiri, Chiraz; Slimani, Yahya

doi:10.1109/cidm.2011.5949436

Cited by 1 publication

(7 citation statements)

References 13 publications

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“…In [4], we have introduced an improved AC-projection algorithm for graphs (based on the AC3 algorithm [15]). The AC-projection algorithm takes two graphs G 1 and G 2 and tests whether there is an AC-projection from G 1 into G 2 .…”

Section: The Ac-projection Algorithm Outlinementioning

confidence: 99%

“…The AC-reduction algorithm [4] is able to construct the AC-reduced graph considering any graph G. To do this, the algorithm does an auto AC-projection G G and then make the necessary merges. Thus, this algorithm is very simple and has a polynomial complexity, since the AC-projection's complexity is polynomial.…”

Section: The Ac-reduction Algorithm Outlinementioning

confidence: 99%

“…The first instance, named FGMAC [4], follows a breadth-first order to find frequent subgraphs and uses an Apriori-like [1] search strategy. The second, named AC-miner [5], is a patterngrowth approach that follows a depth-first search space exploration strategy and uses powerful pruning techniques in order to considerably reduce this search space.…”

Section: Pattern-growth Strategymentioning

confidence: 99%

“…The second is a pattern-growth approach that follows a depth-first search space exploration strategy and uses powerful pruning techniques in order to considerably reduce this search space. This paper is a wide extension of two previous works we carried out [4,5]. The present work defines a generic framework that generalizes the two instances presented in [4] and [5].…”

mentioning

confidence: 91%

“…This paper is a wide extension of two previous works we carried out [4,5]. The present work defines a generic framework that generalizes the two instances presented in [4] and [5]. We have specified this framework after a thorough analysis of the characteristics of the two approaches.…”

mentioning

confidence: 93%

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LC-mine: a framework for frequent subgraph mining with local consistency techniques

et al. 2014

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Developing algorithms that discover all frequently occurring subgraphs in a large graph database is computationally extensive, as graph and subgraph isomorphisms play a key role throughout the computations. Since subgraph isomorphism testing is a hard problem, fragment miners are exponential in runtime. To alleviate the complexity issue, we propose to introduce a bias in the projection operator and instead of using the costly subgraph isomorphism projection, one can use a polynomial projection having a semantically valid structural interpretation. In this paper, our purpose is to present LC-mine, a generic and efficient framework to mine frequent subgraphs by the means of local consistency techniques used in the constraint programming field. Two instances of the framework based on the arc consistency technique are developed and presented in this paper. The first instance follows a breadthfirst order, while the second is a pattern-growth approach that follows a depth-first search space exploration strategy. Then, we prove experimentally that we can achieve an important performance gain without or with nonsignificant loss of discovered patterns in terms of quality.

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Section: The Ac-projection Algorithm Outlinementioning

confidence: 99%

Section: The Ac-reduction Algorithm Outlinementioning

confidence: 99%