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2021 | OriginalPaper | Chapter

Mining Frequent Patterns from Hypergraph Databases

Authors : Md. Tanvir Alam, Chowdhury Farhan Ahmed, Md. Samiullah, Carson K. Leung

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

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Abstract

The chapter begins by discussing the inadequacies of graphs in modeling complex relationships and introduces hypergraphs as a superior data structure. It delves into the problem of extracting interesting patterns from hypergraph databases and proposes a new framework for frequent hypergraph mining. The framework decomposes hyperedges to capture more meaningful patterns and is supported by an efficient algorithm, FHGM, which uses depth-first search and canonical labeling to prune the search space. The algorithm is evaluated through experiments on real-life datasets, demonstrating significant improvements in runtime and classification accuracy compared to existing methods.
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next chapter Discriminating Frequent Pattern Based Supervised Graph Embedding for Classification
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Metadata
Title
Mining Frequent Patterns from Hypergraph Databases
Authors
Md. Tanvir Alam
Chowdhury Farhan Ahmed
Md. Samiullah
Carson K. Leung
Copyright Year
2021
Book
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-3-030-75764-9

Electronic ISBN: 978-3-030-75765-6

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-75765-6_1

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