A quickstart in frequent structure mining can make a difference

By Siegfried Nijssen, Joost N. Kok. 2004

561 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

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Citations in the corpus

Text Categorization as a Graph Classification Problem (2015, methodology): “Some popular graph pattern mining algorithms are gSpan (Yan and Han, 2002), FFSM (Huan et al., 2003) and Gaston (Nijssen and Kok, 2004).”
In silico toxicology: computational methods for the prediction of chemical toxicity (2016, methodology): “43 Signi ﬁ cant substructures capable of discriminating between toxic and non-toxic chemicals can be extracted using an emerging chemical pattern approach as explained in ref.”
Frequent sub-structure-based approaches for classifying chemical compounds (2005, methodology): “These are the AGM algorithm developed by Inokuchi et al. [40], the FSG algorithm developed by members of our group [47], [50], the chemical substructure discovery algorithm developed by Borgelt and Berthold [19], the gSpan algorithm developed by Yan and Han [82], the FFSM by Huan et al. [39], and, m”
Comparison of descriptor spaces for chemical compound retrieval and classification (2006, background): “Molecular Design Limited (MDL) created the key based ﬁngerprints (Maccs Keys) [4] based on pattern matching of a chemical compound structure to a pre-deﬁned set of structural fragments that have been identiﬁed by domain experts [18].”
A survey of frequent subgraph mining algorithms (2012, background): “• GASTON integrates frequent path, subtree, and subgraph mining into one algorithm, due to the observation that most frequent sub-structures in molecular databases are free trees (by Nijssen & Kok, 2004).”
Frequent pattern mining: current status and future directions (2007, methodology): “Pattern-growth-based graph pattern mining algorithms include gSpan by Yan and Han (2002), MoFa by Borgelt and Berthold (2002), FFSM by Huan et al. (2003), SPIN by Huan et al. (2004), and Gaston by Nijssen and Kok (2004).”
Efficiently mining frequent trees in a forest: algorithms and applications (2005, methodology): “The GASTON method [25] adopts an interesting step-wise approach using a combination of path, free tree, and, finally, graph mining to discover all frequent subgraphs.”
gBoost: a mathematical programming approach to graph classification and regression (2009, methodology): “Frequent substructure mining methods, such as AGM (Inokuchi 2005), Gaston (Nijssen and Kok 2004) or gSpan (Yan and Han 2002a), have been applied to enumerate frequently appearing subgraph patterns.”
Near-optimal Supervised Feature Selection among Frequent Subgraphs (2009, methodology): “To avoid this overhead, non-Apriori-based algorithms such as gSpan [34], MoFa [2], FFSM [14], and Gaston [24] adopt the patterngrowth methodology, which attempts to extend graphs from a single subgraph directly.”
Frequent substructure-based approaches for classifying chemical compounds (2003, methodology): “[39], and, more recently, the algorithm by Nijssen and Kok [65].”
GADDI: distance index based subgraph matching in biological networks (2009, background): “As a result, graph database research has attracted a large amount of attention from the database and data mining communities, such as subgraph search in a database of multiple graphs [5, 23, 38, 46, 51, 52, 53, 16], approximate subgraph matching [17, 40, 44, 47, 48, 11], frequent subgraph mining [21”
A classification for community discovery methods in complex networks (2011, background): “Some examples of graph mining algorithms are [5, 159, 160, 161].”
Frequent Subtree Mining – An Overview (2004, unknown): “Tree mining as a step towards efﬁcient graph mining Nijssen et al. [33] have investigated the use of tree mining principles to deal with the more general problem of graph mining.”