Data mining (also called knowledge discovery in databases) represents the process of extracting interesting and previously unknown knowledge (patterns) from data. An association rule expresses the dependence of a set of attribute-value pairs, called items, upon another set of items (itemset). This paper presents a comparative study of the most important iterative algorithms used in association rules mining. A performance study that shows the advantages and disadvantage of the iterative algorithms Apriori, Sampling and Partitioning is also presented.
Data Mining, Iterative Association Rules Mining Algorithms, Apriori, Sampling, Partitioning, Comparison Framework
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