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Explainable FP-Growth/Evidence
Method evidence record

Explainable FP-Growth

Explainable FP-Growth augments the classic FP-Growth frequent-pattern mining algorithm with post-hoc interpretability tools — such as rule importance scores, visual pattern trees, and counterfactual explanations — so analysts can not only discover frequent itemsets and association rules but also understand why specific patterns matter, which items drive rule confidence, and how to communicate findings transparently to stakeholders.

Sources recorded, not reviewed

Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Explainable Frequent Pattern Growth (XAI-Augmented FP-Growth)
Taxonomic method record · ml-model / machine-learning
  • Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12. · DOI 10.1145/335191.335372
  • Association rule learning. Wikipedia. · URL
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Curated claims

Claims persisted in the evidence ledger, each with its own assessment.

No curated claims yet

This view does not invent a claim assessment when the ledger has none.

Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Taxonomic bucketApriori Algorithmmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketAssociation Rulesmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketExplainable Association Rulesmachine-suggested · Relational suggestion, not evidence.Same method familyFP-Growthmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSemi-supervised FP-growthmachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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