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Apriori

Algorithms AIMech:Apriori DRAFT

Frequent-itemset mining using breadth-first candidate enumeration and support-based pruning.

Identity Notes & Concept Scope AssociationRuleLearning does not identify breadth-first frequent-itemset mining. Mining itemsets and generating association rules are distinct tasks; an intermediate parent may be needed.

Claims & Grounded Evidence

1 Claim

Mechanism #mechanism

A breadth-first subset-lattice search prunes candidate itemsets that cannot meet minimum support.

supports Source: Apriori: Frequent Item Set Mining · Locator: Author Apriori implementation page, Introduction: breadth-first traversal of the subset lattice and minimum-support test

⚠️ Curated Limitations & Negative Results

This record supports method identity and the stated mechanism only; no universal performance or biological efficacy claim is curated.
Proposed placement is an inference from the existing AIO hierarchy; maintainers must resolve task, algorithm and model scope before acceptance. Frequent-itemset mining and association-rule induction are different operations; an intermediate parent may be needed.

Artificial Intelligence Ontology (AIO) Grounding

Mapped AIO Classes

AIO Target Class	Mapping Relation	Mapping Rationale
`AIO:AssociationRuleLearning` Association Rule Learning	related_match	AssociationRuleLearning does not identify breadth-first frequent-itemset mining. Mining itemsets and generating association rules are distinct tasks; an intermediate parent may be needed. Mapping is local curation, not an upstream subclass assertion.

Evidence Sources & Primary Literature

1 Source

Source ID	Publication / Document	Type / Access	Reference & Checksum
`method-source`	Apriori: Frequent Item Set Mining Christian Borgelt	official documentation documentation

Curation Provenance

Curator: Codex

Method: ai_assisted

Model: GPT-6

Created: 2026-10-11T03:01:34Z

Updated: 2026-10-11T03:01:34Z

Source File: data/curated/Apriori.yaml