Abstract
In mathematics and graph theory, the graph partitioning problem defines the reduction of graphs into smaller graphs by partitioning its set of nodes into mutually exclusive groups. Fundamentally, finding a partition that simplifies a graph is very hard to figure out and hence, the problem falls under the NP-hard category. Numerous algorithms and mechanisms exist for the evaluation of graph partitioning. In this paper, we have discussed a novel partitioning process named pairwise partitioning, where the prime focus is to derive all possible pairs of vertices to simplify any graph. The process of partitioning has been developed using an equality-inequality mechanism. The entire graph will be decomposed into meaningful pairs represented through certain sets where each set must contain distinct pairs of vertices. Nowadays, data management is an essential task to be performed. To deal with a large amount of data, it should be very difficult and next to impossible. In this situation, our proposed algorithm (pairwise partitioning algorithm) can provide a good solution. As we can simplify a graph in the form of pairs, so, a better understanding should be developed to analyze any graph. Throughout this paper, we have discussed the working principle of the pairwise partitioning algorithm and its significant impact on big data analysis.
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Bhattacharya, I., Gupta, S. (2021). A Novel Partitioning Algorithm to Process Large-Scale Data. In: Pan, I., Mukherjee, A., Piuri, V. (eds) Proceedings of Research and Applications in Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 1355. Springer, Singapore. https://doi.org/10.1007/978-981-16-1543-6_15
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DOI: https://doi.org/10.1007/978-981-16-1543-6_15
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