Efficient frequent subtree mining beyond forests /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Welke, Pascal
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam : IOS Press, 2020.
Colección:Frontiers in artificial intelligence and applications. Dissertations in artificial intelligence ; 348.
Temas:
Data mining.
Data Mining
Exploration de données (Informatique)
Data mining
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Title Page
  • Contents
  • 1 Introduction
  • 1.1 A Motivating Experiment
  • 1.2 Contributions
  • 1.3 Outline
  • 1.4 Previously Published Work
  • 2 Preliminaries
  • 2.1 Notions and Notation
  • 2.2 Frequent Connected Subgraph Mining
  • 2.3 Embedding Computation
  • 2.4 Datasets
  • 3 Related Work
  • 3.1 Algorithms for the SubgraphIsomorphism Problem
  • 3.2 Algorithms for the FCSM Problem
  • 4 Probabilistic Frequent Subtrees
  • 4.1 Mining Probabilistic Frequent Subtrees
  • 4.2 Experimental Evaluation
  • 4.3 Summary
  • 5 Boosted Probabilistic Frequent Subtrees
  • 5.1 An Efficient Embedding Operator for Trees
  • 5.2 Mining Boosted Probabilistic Frequent Subtrees
  • 5.3 Exact Frequent Subtree Mining
  • 5.4 Summary and Open Questions
  • 6 Fast Computation
  • 6.1 Complete Embeddings into Subtree Feature Spaces
  • 6.2 Min-Hashing in Subtree Feature Spaces
  • 6.3 Experimental Evaluation
  • 6.4 Summary and Open Questions
  • 7 Conclusion
  • 7.1 Discussion
  • 7.2 Outlook
  • A HamiltonianPath for Cactus Graphs
  • A.1 Three Necessary Conditions
  • A.2 A Linear Time Algorithm for Cactus Graphs
  • A.3 Some Statistics for Real-World Datasets
  • A.4 Summary
  • B Poissons Binomial Distribution
  • Bibliography

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