EXTRACTING ASSOCIATION RULES FROM DISTRIBUTED ASSOCIATION RULES

Tahrir Saleh Kadoori, Rawia; Kadum Jabbar Alfatly, Emad

	EXTRACTING ASSOCIATION RULES FROM DISTRIBUTED ASSOCIATION RULES
Iraqi Journal of Science
Article 1, Volume 49, Issue 1, 2008, Pages 220-230
Authors
Rawia Tahrir Saleh Kadoori; Emad Kadum Jabbar Alfatly
Abstract
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 required execution time. The algorithm consist of two parts: Part one: Extracting Association Rules for Distributed Association Rules (EAR4DAR) Algorithm; aims to extract association rules for distributed association rules instead of extracting the association 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 them in a file. These Local Association Rules turn in series of operations to produce association rules over the whole distributed systems. Part two: Association Rules_map (AR_map) algorithm aims to get association rules by using AND logic operation which is suitable for representing association relations between items,since it gives indication for finding a relation or not. Additionally, this algorithm uses Karnough_map (K_map) propriety to reduce the duplicate and to generate accurate and logical results with saving time and storage space.

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