Traditional data mining techniques predict frequent itemsets without considering the temporal data. Due to this, efficiency of the frequent itemsets mining is not upto the mark on the temporal data. A new extended apriori algorithm proposed in this research work handles the time interval while identifying the frequent itemsets. The main objective of this research work is to identify patternset in periodic intervals from the temporal data sets. Datasets from UCI data repository is subjected to this proposed method. Experimental results are tabulated and plotted. The results show improvement over the traditional apriori algorithm.

Data mining, Apriori Algorithm, Frequent Itemsets, Temporal Data.

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