RAF-Ready Folktables: Processed Data for Fair Clustering Evaluation

RAF-Ready Folktables is a processed dataset used in the paper 'Retrieval-Augmented Dataset Assembly for Fair Clustering'. The data were prepared by author pengyueli for evaluating RAF, a framework for retrieving external samples to reduce minority under-representation in clustering tasks. The dataset was last updated on 2026-05-15.

Use Cases

• Evaluating retrieval-augmented dataset assembly frameworks based on the RAF methodology described.
• Benchmarking fair clustering algorithms on processed data designed to mitigate under-representation.
• Studying data-level interventions for algorithmic fairness based on the described framework.

Strengths

• Dataset is specifically processed for evaluating a published fair clustering framework, RAF.
• Data are prepared to reduce minority under-representation, a key focus of the associated research.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Processed from the Folktables dataset, as referenced in the associated paper.

Collection Method

Data were processed for the RAF evaluation framework, likely involving retrieval and selection of external samples.

Freshness

Last updated 2026-05-15 02:25:51.