FARM: An FCA-based Association Rule Miner

摘要

Association rule mining is a well-researched and widely applied data mining technique for discovering regularities between items in a dataset. An association rule consists of an antecedent and a consequent with two measures, named support and confidence, which indicate how valuable the rule is. For several decades, intensive studies have been made on efficient association rule mining methods aiming to reduce rule-extraction time and to prevent generation of redundant rules. By incorporating negation and disjunction operators into antecedents, our study offers richer expressive power in describing user interests as antecedents, which in turn translates into more valuable association rules whose consequents match the expressed user interests. This study consists of three components: (1) a conceptual model, called plant, that represents necessary constituents for the proposed extended association rules; (2) three algorithms, called CULTIVATION algorithm family, that demonstrate how the extended association rule is processed within the model; (3) a full-fledged Java-based system, called FARM (FCA-based Association Rule Miner), that is a computerized implementation of our approach. Finally, in order to verify the efficiency and usefulness of our approach, experiments were carried out that compared the approach with extant representative methods.