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Discovering Interesting Patterns from Hypergraphs
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/int.22269openalex-snapshot
Efficiently mining erasable stream patterns for intelligent systems over uncertain data
inferredcites →https://doi.org/10.1007/3-540-45372-5_2openalex-snapshot
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
inferredcites →https://doi.org/10.1007/978-3-030-25070-6openalex-snapshot
Algorithms and Models for the Web Graph
inferredcites →https://doi.org/10.1007/978-3-030-75765-6_1openalex-snapshot
Mining Frequent Patterns from Hypergraph Databases
inferredcites →https://doi.org/10.1007/978-3-030-75765-6_2openalex-snapshot
Discriminating Frequent Pattern Based Supervised Graph Embedding for Classification
inferredcites →https://doi.org/10.1007/978-3-319-93040-4_52openalex-snapshot
WFSM-MaxPWS: An Efficient Approach for Mining Weighted Frequent Subgraphs from Edge-Weighted Graph Databases
inferredcites →https://doi.org/10.1007/978-3-540-68125-0_61openalex-snapshot
A Tree-Based Approach for Frequent Pattern Mining from Uncertain Data
inferredcites →https://doi.org/10.1007/978-3-540-71701-0_8openalex-snapshot
Mining Frequent Itemsets from Uncertain Data
inferredcites →https://doi.org/10.1007/978-3-540-73847-3_26openalex-snapshot
Frequent Hypergraph Mining
inferredcites →https://doi.org/10.1007/978-3-642-15105-7_7openalex-snapshot
Frequent Sub-graph Mining on Edge Weighted Graphs
inferredcites →https://doi.org/10.1007/978-3-642-37453-1_2openalex-snapshot
PUF-Tree: A Compact Tree Structure for Frequent Pattern Mining of Uncertain Data
inferredcites →https://doi.org/10.1007/b107037openalex-snapshot
Graph-Based Representations in Pattern Recognition : 5th IAPR International Workshop, GbRPR 2005, Poitiers, France, April 11-13, 2005, Proceedings
inferredcites →https://doi.org/10.1007/bfb0014139openalex-snapshot
Advances in Database Technology — EDBT '96
inferredcites →https://doi.org/10.1007/s10489-022-03385-8openalex-snapshot
UGMINE: utility-based graph mining
inferredcites →https://doi.org/10.1016/j.eswa.2019.113087openalex-snapshot
Efficient approach for incremental weighted erasable pattern mining with list structure
inferredcites →https://doi.org/10.1016/j.knosys.2022.108134openalex-snapshot
Advanced uncertainty based approach for discovering erasable product patterns
inferredcites →https://doi.org/10.1093/bioinformatics/bth919openalex-snapshot
An efficient algorithm for detecting frequent subgraphs in biological networks
inferredcites →https://doi.org/10.1109/icde.2001.914830openalex-snapshot
PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth
inferredcites →https://doi.org/10.1109/icdm.2002.1184038openalex-snapshot
gSpan: graph-based substructure pattern mining
inferredcites →https://doi.org/10.1109/icdmw.2009.12openalex-snapshot
Weighted Frequent Subgraph Mining in Weighted Graph Databases
inferredcites →https://doi.org/10.1109/icett.2016.7873705openalex-snapshot
A distributed approach to weighted frequent Subgraph mining
inferredcites →https://doi.org/10.1109/tie.2019.2956387openalex-snapshot
Uncertainty-Based Pattern Mining for Maximizing Profit of Manufacturing Plants With List Structure
inferredcites →https://doi.org/10.1109/tkde.2005.127openalex-snapshot
Frequent substructure-based approaches for classifying chemical compounds
inferredcites →https://doi.org/10.1109/tkde.2013.124openalex-snapshot
Mining Probabilistically Frequent Sequential Patterns in Large Uncertain Databases
inferredcites →https://doi.org/10.1137/1.9781611975321.35openalex-snapshot
Learning Graph Representation via Frequent Subgraphs
inferredcites →https://doi.org/10.1145/1014052.1014088openalex-snapshot
Scalable mining of large disk-based graph databases
inferredcites →https://doi.org/10.1145/1460797.1460799openalex-snapshot
Mining frequent cross-graph quasi-cliques
inferredcites →https://doi.org/10.1145/1645953.1646028openalex-snapshot
Frequent subgraph pattern mining on uncertain graph data
inferredcites →https://doi.org/10.1145/1951365.1951408openalex-snapshot
Efficient discovery of frequent subgraph patterns in uncertain graph databases
inferredcites →https://doi.org/10.1145/3442590openalex-snapshot
TipTap: Approximate Mining of Frequent k -Subgraph Patterns in Evolving Graphs
inferredcites →https://doi.org/10.1145/3494567openalex-snapshot
Hypergraph Convolution on Nodes-Hyperedges Network for Semi-Supervised Node Classification
inferredcites →https://doi.org/10.1145/974614.974655openalex-snapshot
Mining protein family specific residue packing patterns from protein structure graphs
inferredcites →https://doi.org/10.3115/v1/p15-1164openalex-snapshot
Text Categorization as a Graph Classification Problem
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Anomaly Detection with Generalized Isolation Forest
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inferred← citeshttps://doi.org/10.1007/s11761-025-00470-xopenalex-snapshot
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inferred← citeshttps://doi.org/10.1109/bibe60311.2023.00052openalex-snapshot
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Multiple Multi-Modal Methods of Malignant Mammogram Classification
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Visual Analytics of Motor Vehicle Accidents
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An Explainable Artificial Intelligence Solution for the Practical Application of Employee Turnover
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A Visual Analytics Solution for Analyzing and Mining Infectious Disease Data
inferred← citeshttps://doi.org/10.1109/sds60720.2024.00011openalex-snapshot
Mining and Forecasting Energy Consumption Based on Weather Data
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