  Frequent Subgraph Mining (FSM) is the process of identifying common subgraph
patterns that surpass a predefined frequency threshold. While FSM is widely
applicable in fields like bioinformatics, chemical analysis, and social network
anomaly detection, its execution remains time-consuming and complex. This
complexity stems from the need to recognize high-frequency subgraphs and
ascertain if they exceed the set threshold. Current approaches to identifying
these patterns often rely on edge or vertex extension methods. However, these
strategies can introduce redundancies and cause increased latency. To address
these challenges, this paper introduces a novel approach for identifying
potential k-vertex patterns by combining two frequently observed (k - 1)-vertex
patterns. This method optimizes the breadth-]first search, which allows for
quicker search termination based on vertices count and support value. Another
challenge in FSM is the validation of the presumed pattern against a specific
threshold. Existing metrics, such as Maximum Independent Set (MIS) and Minimum
Node Image (MNI), either demand significant computational time or risk
overestimating pattern counts. Our innovative approach aligns with the MIS and
identifies independent subgraphs. Through the "Maximal Independent Set" metric,
this paper offers an efficient solution that minimizes latency and provides
users with control over pattern overlap. Through extensive experimentation, our
proposed method achieves an average of 10.58x speedup when compared to GraMi
and an average 3x speedup when compared to T-FSM
