Identification of Best Algorithm in Association Rule Mining Based on Performance?

Data Mining finds hidden pattern in data sets and association between the patterns. To achieve the objective of data mining association rule mining is one of the important techniques. Association rule mining is a particularly well studied field in data mining given its importance as a building block in many data analytics tasks. Many studies have focused on efficiency because the data to be mined is typically very large. This paper presents a comparison on three different association rule mining algorithms i.e. FP Growth, Apriori and Eclat. The time required for generating frequent itemsets plays an important role. This paper describes implementations of these three algorithms that use several optimizations to achieve maximum performance, w.r.t. execution time. The comparison of algorithms based on the aspects like different support and confidence values.