A Survey on Iterative Mapreduce based Frequent Subgraph Mining Algorithm with Load Balancing

In recent years, the "Big Data" phenomenon has immersed countless and application areas including information mining, computational science, ecological sciences, e-business, web mining, and interpersonal organization examination. Frequent Sub graph Mining (FSM) is an essential undertaking for exploratory information examination on diagram information particularly when the chart is tremendous. In the late years, numerous calculations have been proposed to tackle this undertaking. These calculations expect that the mining task’s information structure is sufficiently little to fit in the principle memory in the frameworks. However, as the real-world graph data grows, both in amount and size, such a suspicion couldn't be met. To overcome this, some diagram database-driven strategies have been proposed in genuine issue for settling FSM; in any case, a conveyed arrangement utilizing MapReduce worldview has not been investigated broadly. Since MapReduce is turning into the accepted worldview for calculation on huge information, an efficient FSM calculation on this worldview is of immense interest.