{"paper":{"title":"Specious rules: an efficient and effective unifying method for removing misleading and uninformative patterns in association rule mining","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Geoffrey I. Webb, Wilhelmiina H\\\"am\\\"al\\\"ainen","submitted_at":"2017-09-12T15:39:47Z","abstract_excerpt":"We present theoretical analysis and a suite of tests and procedures for addressing a broad class of redundant and misleading association rules we call \\emph{specious rules}. Specious dependencies, also known as \\emph{spurious}, \\emph{apparent}, or \\emph{illusory associations}, refer to a well-known phenomenon where marginal dependencies are merely products of interactions with other variables and disappear when conditioned on those variables.\n  The most extreme example is Yule-Simpson's paradox where two variables present positive dependence in the marginal contingency table but negative in al"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.03915","kind":"arxiv","version":1},"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"}