Web oriented FIM for large scale dataset using hadoop

In large scale datasets, mining frequent itemsets using existing parallel mining algorithm is to balance the load by distributing such enormous data between collections of computers. But we identify high performance issue in existing mining algorithms [1]. To handle this problem, we introduced a new approach called data partitioning using Map Reduce programming model. In our proposed system, we have introduced new technique called frequent itemset ultrametric tree rather than conservative FP-trees. An investigational outcome tells us that, eradicating redundant transaction results in improving the performance by reducing computing loads.