Performance Evaluation of Apriori and FP-Growth Algorithms

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Provided by: International Journal of Computer Applications
Topic: Data Management
Format: PDF
In data mining, association rule mining is a standard and well researched technique for locating fascinating relations between variables in large databases. Association rule is used as a precursor to different data mining techniques like classification, clustering and prediction. This paper is to gauge the performance of the apriori algorithm and Frequent Pattern (FP) growth algorithm by comparing their capabilities. The evaluation study shows that the FP-growth algorithm is efficient and ascendable than the apriori algorithm.
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