Author : K.Premchander 1
Date of Publication :18th April 2018
Abstract: Frequent Pattern Mining (FPM) is one of the most well-known techniques to extract frequent patterns from data. It plays an important role in association rule mining, finding correlations and trends etc. Finding Frequent Patterns becomes a very difficult task when they are applied to Big Data. Many researchers have proposed many algorithms to generate FIM, but the execution time and storage space plays a key difference .All the existing algorithms hold well only when the dataset is small. So there is a need to propose an efficient algorithm to find frequent itemsets from Big Dataset using constraints. In almost all FPM algorithms, Frequent 1-itemsets are generated to find the support count (occurrences) of each item in the entire database In order increase the efficiency of generating FIM, cache is introduced so that the support count can be calculated in the cache itself. For this a Modified Map Reduce algorithm has been proposed.
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