
Efficient Frequent Subtree Mining Beyond Forests
Description
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This book presents a mining system that gives up the demand on the completeness of the pattern set, and instead guarantees a polynomial delay between subsequent patterns. To complement this, efficient methods devised to compute the embedding of arbitrary graphs into the Hamming space spanned by the pattern set are described. As a result, a system is proposed that allows the efficient application of distance-based learning methods to arbitrary graph databases.
In addition to an introduction and conclusion, the book is divided into chapters covering: preliminaries; related work; probabilistic frequent subtrees; boosted probabilistic frequent subtrees; and fast computation, with a further two chapters on Hamiltonian path for cactus graphs and Poisson binomial distribution.
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Content
- 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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