Extended Realization of Associative Property for Data Mining using Apriori Optimization Technique for Frequency Pattern Generation
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Abstract
Mining Association rules in transactional or relational databases have recently attracted a lot of attention in databases
communities. Associative Rule Mining as defined in [13] is a popular and well researched method for discovering interesting
relationship between various items involved in large databases. Introduced association rules for discovering regularities between
products in large-scale transaction data recorded by point-of-sale (POS) systems. Our objective is to find the Frequency Pattern based
upon Apriori optimization technique. An Apriori algorithm is the most commonly used Association Rule Mining. This algorithm
somehow has limitation and thus, giving the opportunity to do this work. This paper introduces a new way in which the Apriori
algorithm can be tested with large number of transactions and item sets.. The modified algorithm introduces factors such as set size and
set size frequency which in turn are being used to eliminate non significant candidate keys. With the use of these factors, the modified
algorithm introduces a more efficient and effective way of minimizing candidate keys.
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Keywords: Data Mining, Apriori algorithm, Frequent items, Set size, Set size frequency, Minimum support.
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