{"paper":{"title":"Finding the True Frequent Itemsets","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DB","cs.DS","stat.ML"],"primary_cat":"cs.LG","authors_text":"Fabio Vandin, Matteo Riondato","submitted_at":"2013-01-07T15:04:43Z","abstract_excerpt":"Frequent Itemsets (FIs) mining is a fundamental primitive in data mining. It requires to identify all itemsets appearing in at least a fraction $\\theta$ of a transactional dataset $\\mathcal{D}$. Often though, the ultimate goal of mining $\\mathcal{D}$ is not an analysis of the dataset \\emph{per se}, but the understanding of the underlying process that generated it. Specifically, in many applications $\\mathcal{D}$ is a collection of samples obtained from an unknown probability distribution $\\pi$ on transactions, and by extracting the FIs in $\\mathcal{D}$ one attempts to infer itemsets that are f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1301.1218","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}