Comparative Study of Frequent Itemset Mining Algorithms Apriori and FP Growth

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Provided by: International Journal of Computer Applications
Topic: Big Data
Format: PDF
Frequent item set mining leads to the discovery of associations among items in large transactional database. In this paper, two algorithms of generating frequent item sets are discussed: apriori and FP-growth algorithm. In apriori algorithm candidates are generated and testing is done which is easy to implement but candidate generation and support counting is very expensive in this because database is checked many times. In the FP-growth, there is no candidate generation and requires only 2 passes over the database but in this the generation of FP-tree become very expansive to build and support is counted only when entire dataset is added to FP-tree.
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