Complete mining of frequent patterns from graphs: Mining graph data

DOI10.1023/A:1021726221443zbMath1033.68079OpenAlexW1592376869MaRDI QIDQ1395734

Hiroshi Motoda, Takashi Washio, Akihiro Inokuchi

Publication date: 1 July 2003

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1023/a:1021726221443

zbMATH Keywords

adjacency matrixdata miningApriori algorithmchemical carcinogenesis analysisgraph dataWeb browsing analysis

Mathematics Subject Classification ID

Graph theory (including graph drawing) in computer science (68R10)

Related Items (18)

GTED: Graph Traversal Edit Distance ⋮ Mining preserving structures in a graph sequence ⋮ Distributed discovery of frequent subgraphs of a network using MapReduce ⋮ Mining spatial colocation patterns: a different framework ⋮ Counting frequent patterns in large labeled graphs: a hypergraph-based approach ⋮ Graph kernels based on tree patterns for molecules ⋮ The graph matching problem ⋮ Graph sampling with applications to estimating the number of pattern embeddings and the parameters of a statistical relational model ⋮ Frequency-based views to pattern collections ⋮ XRules: an effective algorithm for structural classification of XML data ⋮ Efficient Algorithms for Discovering Frequent and Maximal Substructures from Large Semistructured Data ⋮ Network classification with applications to brain connectomics ⋮ Support measures for graph data ⋮ Computational aspects of mining maximal frequent patterns ⋮ ORIGAMI: A Novel and Effective Approach for Mining Representative Orthogonal Graph Patterns ⋮ Generating hypotheses about molecular structure–activity relationships (SARs) by solving an optimization problem ⋮ COOMA: A Components Overlaid Mining Algorithm for Enumerating Connected Subgraphs with Common Itemsets ⋮ Frequent mining of subgraph structures

Uses Software

• nauty

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