Association Rule Mining
Updated 2026-08-10
INTRODUCTION
English translation pending.
CORE DEFINITION
A data mining method that searches transactional records for itemsets that co-occur frequently and converts them into if-then rules. The core proposition is that association strength can be quantified, so rules are ranked by support, which measures how often the pattern appears, confidence, which measures its conditional probability, and lift, which measures the gain over independence. The key qualification is that high support and confidence do not imply usefulness.
SCAFFOLDING EFFECT
Reduce cognitive load
- Threshold setting: fix the minimum support and confidence before mining. - Frequent itemset search: run an algorithm such as Apriori or FP-Growth over the transactions. - Lift filter: keep only rules whose lift shows a real gain over independence.
Anchor fast decisions
Frequent co-occurrence is evidence that items are linked in the process that generated the data, whether that process is shopping, clicking, or diagnosing. Counting co-occurrences over many transactions converts that linkage into a measurable statistic, and the resulting rules make the pattern explicit. Ranking by lift separates genuine association from patterns that merely reflect how common each item already is.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
account_treeGenealogyexpand_more
menu_bookReferencesexpand_more
Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Association_rule_learningverified
PRIVATE NOTES · Only visible to you
SAVED Q&A
ENTRY Q&A · Private saving available
Ask with a clear boundary
thinkingmodels answers from published entry context only.
Your question is sent to thinkingmodels. The answer uses public entry context only.
RELATED MODELS