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For partitioning the dataset of financial ratios into abnormal and normal groups, this paper proposes an integrated clustering method by combining the similarity matching (SM) algorithm with self-organizing maps (SOM), called the SM-SOM method. The hybrid system is enacted in three stages: a preprocessing stage, and similarity matching with the cosine algorithm and then the SOM cluster. For evaluating the performance of this integrated method, experiments were conducted with quarterly financial ratios of the listed electrical manufacturing sector in China. The empirical results indicate that the SM-SOM technique can effectively improve the accuracy rate for clustering the financial data into normal and abnormal groups.
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