Complete Mining of Frequent Patterns from Graphs: Mining Graph Data

By Akihiro Inokuchi, Takashi Washio, Hiroshi Motoda. Machine-mediated learning, 2003

317 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f7da65e5e249083be84071fd244782720d326b6c

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Citations in the corpus

A quickstart in frequent structure mining can make a difference (2004, result): “We compare our algorithm with a broad range of other algorithms: the graph miners gSpan, FSG and AcGM [6], and the FTM free tree miners of R¨uckert [13] and Chi [2].”
Large scale mining and retrieval of visual data in a multimodal context (2008, background): “2) for frequent itemset mining, AGM [Inokuchi et al., 2003] starts from frequent one-vertex graphs and generates candidate graphs of larger sizes by pairwise joining of frequent subgraphs that satisfy the following two conditions:”
A statistical approach to rule learning (2006, methodology): “Top-down propositionalization methods like Warmr (Dehaspe and Toivonen, 1999) and AGM (Inokuchi et al., 2003) generate or refine existing queries syntactically (g(q1, .”
The Multiscale Laplacian Graph Kernel (2016, background): “There is a wide range of problems in applied machine learning from web data mining (Inokuchi et al., 2003) to protein function prediction (Borgwardt et al., 2005) where the input space is a space of graphs.”
Efficiently mining frequent trees in a forest: algorithms and applications (2005, background): “FSM not only encompasses mining techniques like associations [3] and sequences [4], but it also generalizes to more complex patterns like frequent trees and graphs [18], [21].”
A survey of frequent subgraph mining algorithms (2012, background): “Inokuchi et al. have further extended their original work to mine frequent induced subgraphs from general graph databases that can contain directed (or undirected), labelled (or unlabelled) graphs and even loops (Inokuchi et al. 2003).”
Finding Frequent Patterns in a Large Sparse Graph* (2004, methodology): “In recent years, a number of efficient and scalable algorithms have been developed to find patterns in the graph-transaction setting (Borgelt and Berthold, 2002; Yan and Han, 2002; Inokuchi et al., 2003; Hong et al., 2003; Yan and Han, 2003; Huan et al., 2003; Kuramochi and Karypis, 2004a).”