EXTRACTING ASSOCIATION RULES FROM DISTRIBUTED ASSOCIATION RULES - Alfatly, Emad; Kadoori, Rawia (Iraqi Journal of Science, 2024)
Mining for associations rules between items in large transactional distributed databases is a central problem in the field of knowledge discovery. When distributed databases are merged at single machine to mining knowledge it will required large capacity of storage, long execution time in addition to that; transferring a huge volume of data over network might take extremely much time and also require an unbearable financial cost.In this paper proposed algorithm is presented toward saving communication costover the network, central storage cost requirements, and accelerating requiredexecution time. The algorithm consist of two parts: Part one: Extracting AssociationRules for Distributed Association Rules (EAR4DAR) Algorithm; aims to extractassociation rules for distributed association rules instead of extracting theassociation rules from a huge quantity of distributed data located at several sites.This is done by collecting the local association rules from each site and storing themin a file. These Local Association Rules turn in series of operations to produceassociation rules over the whole distributed systems. Part two: AssociationRules_map (AR_map) algorithm aims to get association rules by using AND logicoperation which is suitable for representing association relations between items,since it gives indication for finding a relation or not. Additionally, this algorithmuses Karnough_map (K_map) propriety to reduce the duplicate and to generateaccurate and logical results with saving time and storage space.