Nowadays, there is a large amount of symbolic data in various fields, and people can fully utilize these symbolic data for clustering, providing a better foundation and direction for data mining and analysis. Currently, clustering algorithms for symbol data have emerged one after another, but there are still shortcomings in computational cost and algorithm robustness. Therefore, it is urgent to study an algorithm with stable clustering results, less time consumption, and low I/O overhead. The following proposes a symbolic cluster analysis algorithm that approaches to the optimal value. It reduces the size of the original data by generating a symbolic association graph from a large number of symbolic data samples, effectively solves the problem of high computing costs caused by the huge amount of data, and proves the clustering effect of the algorithm through empirical analysis.
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