Abstract: Frequent item-set generation, a key step in association mining, is the process of generating item-sets that satisfy a minimum support threshold. EFTP, a new frequent temporal pattern mining algorithm proposed in this study, requires lesser number of repeated scans of original input in comparison to apriori principle based algorithms. Experimental results demonstrate the significant betterment in execution time due to reduced number of input scans and support independence of the proposed algorithm as compared to existing Apriori principle based algorithms.