Abstract: Frequent pattern mining has become an important data mining task and has been a focused theme in data mining research. Frequent pattern mining aims to find frequently occurring subsets in sequence of sets. The frequent pattern mining appears as a sub problem in many other data mining fields such as association rules discovery, classification, clustering, web mining, market analysis etc. Different frameworks have been defined for frequent pattern mining. The most common one is the support based framework, in which item sets with frequency above a given threshold are found. This paper presents review of different frequent mining algorithms including Apriori, FP-growth and DIC. A brief description of each technique has been provided. In the last, different frequent pattern mining techniques are compared based on various parameters of importance.
Keywords: Data mining; Frequent patterns; Frequent pattern mining (FPM); support; itemset.