Overview
The inductive database approach to graph mining can be characterized by (1) the concept of querying for (subgraph) patterns in databases of graphs, and (2) the use of specific data structures representing the space of solutions. For the former, a query language for the specification of the patterns of interest is necessary. The latter aims at a compact representation of the solution patterns.
Pattern Domain
In contrast to other graph mining approaches, the inductive database approach to graph mining (De Raedt and Kramer 2001; Kramer et al. 2001) focuses on simple patterns (paths and trees) and complex queries (see below), not on complex patterns (general subgraphs) and simple queries (minimum frequency only). While the first approaches were restricted to paths as patterns in graph databases, they were later extended toward unrooted trees (Rückert and Kramer 2003, 2004). Most of the applications are dealing with structures of small molecules and structure–activity...
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Kramer, S. (2017). Inductive Database Approach to Graphmining. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_391
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_391
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