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Pyaerial: Scalable Association Rule Mining from Tabular Data

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-031-24628-9_16openalex-extracted

Autoencoders

inferredcites →https://doi.org/10.1007/978-3-642-30353-1_6openalex-extracted

Mining Top-K Association Rules

inferredcites →https://doi.org/10.1016/j.ins.2011.05.016openalex-extracted

Generalized association rule mining with constraints

inferredcites →https://doi.org/10.1016/j.ins.2020.10.020openalex-extracted

A guided FP-Growth algorithm for mining multitude-targeted item-sets and class association rules in imbalanced data

inferredcites →https://doi.org/10.1038/s42256-019-0048-xopenalex-extracted

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

inferredcites →https://doi.org/10.1080/08839510903208062openalex-extracted

CONFIDENCE METRICS FOR ASSOCIATION RULE MINING

inferredcites →https://doi.org/10.1109/access.2020.3009006openalex-extracted

Big Data-Driven Abnormal Behavior Detection in Healthcare Based on Association Rules

inferredcites →https://doi.org/10.1137/1.9781611972726.27openalex-extracted

CHARM: An Efficient Algorithm for Closed Itemset Mining

inferredcites →https://doi.org/10.1145/1390156.1390294openalex-extracted

Extracting and composing robust features with denoising autoencoders

inferredcites →https://doi.org/10.1145/3703790.3703834openalex-extracted

3K: Knowledge-Enriched Digital Twin Framework

inferredcites →https://doi.org/10.1177/29498732251377518openalex-extracted

Learning Semantic Association Rules from Internet of Things Data

inferredcites →https://doi.org/10.1186/s40537-022-00592-5openalex-extracted

A systematic review and research perspective on recommender systems

inferredcites →https://doi.org/10.21105/joss.00638openalex-extracted

MLxtend: Providing machine learning and data science utilities and extensions to Python’s scientific computing stack

inferredcites →https://doi.org/10.48550/arxiv.1704.01701openalex-extracted

Learning Certifiably Optimal Rule Lists for Categorical Data

inferredcites →https://doi.org/10.48550/arxiv.2008.12330openalex-extracted

The Impact of Discretization Method on the Detection of Six Types of Anomalies in Datasets

inferredcites →https://doi.org/10.48550/arxiv.2307.00662openalex-extracted

Numerical Association Rule Mining: A Systematic Literature Review

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-981-95-4409-7_16openalex-snapshot

Enhancing Isolation Forest with Rule Based Knowledge from Association Rule Mining

inferred← citeshttps://doi.org/10.1016/j.softx.2025.102341openalex-snapshot

PyAerial: Scalable association rule mining from tabular data

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