Using Fuzzy FP-Growth for Mining Association Rules
Chien-Hua Wang, Zheng Li, Xuelian Yu, XiDuan Zheng · 2017
This paper aims to use fuzzy set theory and FPgrowth derived from fuzzy association rules.At first, we apply fuzzy partition method and decide a membership function of quantitative value for each transaction item.Next, we implement FP-growth to deal with the process of data mining.In addition, in order to understand the impact of fuzzy FP-growth algorithm and other fuzzy data mining algorithms on the execution time and the numbers of generated association rule, the experiment will be performed by using different thresholds.Lastly, the experiment results show fuzzy FP-growth algorithm is more efficient than other existing methods.