Subspace Clustering Method for Analysis of Four-diagnoses of Traditional Chinese Medicine

Abstract

Abstract: The analysis of the four-diagnosis of traditional Chinese medicine is an important part of the analysis of TCM syndromes based on the information of the four-diagnosis. Constructing an effective analysis model of the four-diagnosis of TCM can better mine the correlation between TCM syndromes and provide decision support for the clinical of TCM. In this paper, through the analysis of the CLIQUE algorithm of subspace clustering, combined with the data characteristics of the four-diagnosis information, an improved CLIQUE algorithm (ChM-CLIQUE) based on limited space search strategy is proposed. By optimizing the search strategy of the CLIQUE algorithm and performing a depth-first search centered on the cell with the largest grid density among dense cells, the cluster clusters are generated to improve the performance of the algorithm, and introducing grid adaptation density based on the characteristics of the sample Gaussian distribution in the cluster clusters, the recognition accuracy of cluster boundaries is enhanced. In the experiment, multiple sets of comparative experiments were carried out on the data set collected in the clinical medicine of traditional Chinese medicine. The experimental results show that the contour coefficients of the algorithm in this paper are significantly improved by 12.6% and 19.3% respectively compared with the CLIQUE algorithm.

Key words: four-diagnosis analysis, subspace clustering, CLIQUE algorithm

XU Li-hui, CHEN Min, WANG Chi-she. Subspace Clustering Method for Analysis of Four-diagnoses of Traditional Chinese Medicine[J]. Computer and Modernization, 2020, 0(12): 67-71.

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