OPTIMIZED FREQUENT SUBGRAPH MINING USING ITERATIVE MAPREDUCE FOR ENHANCED SCALABILITY AND PERFORMANCE

Frequent subgraph mining (FSM) is a core graph analysis task arising from many application domains, including bioinformatics, chemoinformatics, and social network analysis. Traditional FSM methods are not scalable to large datasets due to in-memory computations and inefficient candidate pruning, as found in gSpan and Apriori-based techniques. Although some recently distributed approaches, such as G-thinker and FlexMiner, have tried to overcome them, they are still confronted with high computational overhead, excessive data shuffling, and scalability. Such problems necessitate a sound, scalable approach for large-scale graph mining in the modern era. This research proposes a novel, sophisticated framework, and algorithm called Frequent Subgraph Mining Using MapReduce (FSM-MR) with inherent optimizations. This efficient algorithm incorporates mapper combiners for minimizing data shuffling, canonical labeling for avoiding repeated enumeration of identical subgraphs, and dynamic support thresholds for effective pruning. FSM-MR, implemented in a Hadoop environment, shows better performance with up to 50% runtimes shorter than the state-of-the-art methods, with near-linear scalability with the addition of cluster nodes. The ability of the proposed framework to process large-scale graph datasets makes it beneficial for applications involving scalable, efficient graph mining. FSM-MR overcomes methodological limitations in the current state-of-the-art algorithms, helping set the stage for future research in these areas and fostering graph analytics in various scientific and industrial settings.