Data Mining (DM) is the process of automated extraction of interesting data patterns representing knowledge, from the large data sets. Frequent itemsets are the itemsets that appear in a data set frequently. Finding such frequent itemsets plays an essential role in mining associations, correlations, and many other interesting relationships among itemsets in transactional database. In this paper an algorithm, SAR (Strong Association Rule), is designed and implemented to check whether an Association Rule (AR) is strong enough or not. Apriori algorithm is also implemented to generate Frequent k-itemsets. A Binary Transactional Dataset is used for implementing the algorithm in java language.
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