2013/08/10 by Rakesh Duggirala, Duggirala, Rakesh, Pradyumna Narayana +2
Computer Science · Mathematics · Psychology · #97Pxx #Artificial intelligence #Association (psychology) #Association rule learning #Computer science #Data Mining Algorithms and Applications #Data mining #Databases (cs.DB) #Decision rule #FOS: Computer and information sciences #Field (mathematics) #Imbalanced Data Classification Techniques #Mathematics #Psychology #Quality (philosophy) #Rough Sets and Fuzzy Logic #Set (abstract data type) #cs.DB #msc:97Pxx
paper · pdf · doi:10.48550/arxiv.1308.2310
published in arXiv (Cornell University) (Cornell University) · IJCTT-2013
arxiv created 2013/08/10 · openalex publication_date 2013/08/10 · arxiv updated 2013/08/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
In the data mining field, association rules are discovered having domain knowledge specified as a minimum support threshold. The accuracy in setting up this threshold directly influences the number and the quality of association rules discovered. Typically, before association rules are mined, a user needs to determine a support threshold in order to obtain only the frequent item sets. Having users to determine a support threshold attracts a number of issues. We propose an association rule mining framework that does not require a per-set support threshold. Often, the number of association rules, even though large in number, misses some interesting rules and the rules quality necessitates further analysis. As a result, decision making using these rules could lead to risky actions.