Frequent Subtree Mining – An Overview

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Fast rule mining in ontological knowledge bases with AMIE+\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+$$\end{docu (2015, background): “Since RDF data can be seen as a graph, mining frequent subtrees [8,23] is another related field of research.”
AMIE: association rule mining under incomplete evidence in ontological knowledge bases (2013, background): “Since RDF data can be seen as a graph, mining frequent subtrees [6, 18] is another related field of research.”
From Local Patterns to Global Models: The LeGo Approach to Data Mining (2008, background): “The discovery of frequent patterns has been generalized into more elaborate structures such as sequences, trees and graphs, for example with the aim of discovering frequent fragments in molecules (Zaki, 2001; Chi et al., 2005; Sevon et al., 2006; Nijssen & Kok, 2004).”
A survey of frequent subgraph mining algorithms (2012, methodology): “DFCS’s variants can also be found in Chi et al. (2003,2004b).”
Automatic lymphoma classification with sentence subgraph mining from pathology reports. (2014, unknown): “Various graph encodings, enumeration strategies and search pruning policies have been proposed to improve the efficiency of the mining algorithms [44][45].”