Giraph Dynamic Sized Structure Recurrent Subgraph Generation Algorithm for Frequent Subgraph Mining

Data Mining has a subpart called Frequent Subgraph Mining (FSM) and is a demanding area for the implementation of graph classification and graph clustering which is used in the area of the social network, chemical compounds, and biological datasets, enterprise world. Many research workers have been researching on how to produce an effective and optimized technique to extract the candidate subgraphs by eliminating duplicates for the last few decades. In the case of the Giraph distributed system, a different format for input and output classes is required to take graphs into memory and put graphs after completion of its operation, which leads to excessive memory exhaustion. In this paper, a novel methodology “Giraph Dynamic Sized Structure Frequent Subgraph Mining (GDSSFSM)” has been developed to reduce the memory necessity for FSM in a graph-distributed system. The proposed approach reorganizes the inner input format class (i.e. setEdgeInputFormatClass) without any changes. Hence, it can be used by default in a customized format. The experimental analysis is done on the different datasets with an existing algorithm based on execution time and memory requirements and concludes that it decreases up to on average 52% depending on the dataset and the graph (i.e., PageRank, Connected Components, and Simple Shortest Path) edge-centric algorithm. The proposed algorithm can be used in various fields of graph mining such as social networks, bioinformatics, and web data mining