MACHINE LEARNING APPROACH FOR ASSOCIATION RULE MAINTENANCE AND REFINEMENT USING INCREMENTAL DATA WITH UPDATING SUPPORT THRESHOLDS

Association Rule Mining is an important field in knowledge mining that allows the rules of association needed for decision-making with the help of
machine learning approaches. Frequent mining of objects presents a difficulty to huge datasets. As the dataset gets bigger and more time and burden
to uncover the rules. In this invention, overhead and time-consuming overhead reduction techniques with an IPOC (Incremental Pre-ordered code)
tree structure using ML were examined. For the frequent usage of database mining items, those techniques require highly qualified data structures.
FIN(Frequent itemset-Nodeset) employs a node-set, a unique and new data structure to extract frequently used Items, and an IPOC tree to store
frequent data progressively. Different methods have been modified to analyze and assess time and memory use in different data sets. The strategies
suggested and executed shows increased performance when producing rules, using time and efficiency.