Abstract
Frequent itemset (or frequent pattern) mining is a very important issue in the data mining field. Both syntactic simplicity and descriptive potential, are the key features of the itemset-based pattern which have led to its widespread use in a growing number of real-life domains. Many efficient algorithms like Apriori & FP-Growth Algorithm are used to find frequent itemsets from large database. In almost all Frequent Pattern mining algorithms generating Frequent 1-itemsets are generated in order to find the support count(occurences) of each item in the entire transactions. This task is itself a tedious task in generating Frequent Patterns when considering the hugeness of modern databases available. No explicit strategy has been outlined in these algorithms to perform the aforesaid task. In this paper an efficient tree called Support Count tree has been proposed to perform this task. This algorithm can be easily embedded into any of the existing algorithms aimed at frequent pattern mining. With the help of this tree Frequent 1- Itemsets are found out quickly and efficiently which in-turn speeds up the generation of Frequent Patterns of the entire database.
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