PMCS: Partition-Based Maximal Frequent Subgraph Mining Using MCS
Vani Sancheti, Lini T. Thomas, Vikram Pudi · 2024
Current algorithms for Maximal Frequent Sub-graph (MFS) mining do not scale to large databases with more than 300k graphs. Frequency computation is commonly done using numerous sub graph isomorphism operations, which are computationally expensive. This paper explores a partition-based secondary memory algorithm, PMCS, that can make MFS mining for large databases viable. PMCS intelligently avoids redundant computations to perform frequency computation operations optimally. Its intermediate results are also small enough to fit in the main memory. Our results demonstrate that PM CS scales to databases of up to 1000k graphs with an average of 25–30 edges per graph.