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Data mining discovers hidden pattern in data sets and association between the patterns. In this association, rule mining is one of the techniques used to achieve the objective of data mining. These rules are effectively used to uncover unknown relationships producing result that can give the authors a basis for forecasting and decision making. To discover these rules, they have to find out the frequent item sets because these item sets are the building blocks to obtain association rules with a given confidence and support. In this paper, they theoretical analyses the extraction for frequent item sets and compare these algorithm.
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