Performance Comparison of Apriori, Eclat and Fp-growth Algorithm for Association Rule Learning
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چکیده
The main aim is to generate a frequent itemset. Big Data analytics is the process of examining big data to uncover hidden patterns. Association Rule Learning is a technique which is used to implement big data. It finds the frequent items in the dataset. Frequent itemsets are those items which occur frequently in the database. To find the frequent itemsets, we are using three algorithms APRIORI ALGORITHM, ECLAT ALGORITHM and FP-GROWTH ALGORITHM. The performance of these three algorithms is compared based on the Time efficiency. After the comparison, we conclude that APRIORI algorithm is the fastest algorithm for large dataset and FP-GROWTH algorithm is the fastest algorithm for small dataset. It takes less time to generate the frequent item-sets as compared to other algorithm, that is, ECLAT algorithm. Keywords— Big Data analytics, Association Rule Learning, frequent item-sets
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