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Enhanced Association Rule Mining Algorithm (EARMA) for Reducing Computational Time on Large Data Set

priyanka rana, Jaspreet Singh, Dr. Shashi Bhushan

Abstract


Association rule learning is a trendy process for discovering exciting relationships between variables in big database. It is frequently used in market basket analysis field e.g. if a buyer buys onions and potatoes then he also purchases beef. But, in fact, it can be implemented in different application area where we want to determine the association among variables. The APRIORI method is definitely the trendiest. But, even with its good quality property, this procedure has a drawback: the number of obtained rules can be very high. The capabilities to highlight the most exciting rules, those which are related, become a major challenge.

Cite this Article
Priyanka Rana, Jaspreet Singh, Shashi Bhushan. Enhanced Association Rule Mining Algorithm (EARMA) for Reducing Computational Time on Large Data Set. Research & Reviews: Discrete Mathematical Structures. 2016; 3(2): 20–25p.


Keywords


Data mining, utility mining, high utility item set mining, frequent item set

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References


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