Research on Data Analysis of Traditional Chinese Medicine with Improved Differential Evolution Clustering Algorithm.

Honglei Zhu,Yulong Xu,Xueyun Wang,Yingying Zhao,Xiaoxia Yin

doi:10.1155/2021/4468741

Abstract

Medical data analysis is an important part of intelligent medicine, and clustering analysis is a commonly used method for data analysis of Traditional Chinese Medicine (TCM); however, the classical K-Means algorithm is greatly affected by the selection of initial clustering center, which is easy to fall into the local optimal solution. To avoid this problem, an improved differential evolution clustering algorithm is proposed in this paper. The proposed algorithm selects the initial clustering center randomly, optimizes and locates the clustering center in the process of evolution iteration, and improves the mutation mode of differential evolution to enhance the overall optimization ability, so that the clustering effect can reach the global optimization as far as possible. Three University of California, Irvine (UCI), data sets are selected to compare the clustering effect of the classical K-Means algorithm, the standard DE-K-Means algorithm, the K-Means++ algorithm, and the proposed algorithm. The experimental results show that, in terms of global optimization, the proposed algorithm is obviously superior to the other three algorithms, and in terms of convergence speed, the proposed algorithm is better than DE-K-Means algorithm. Finally, the proposed algorithm is applied to analyze the drug data of Traditional Chinese Medicine in the treatment of pulmonary diseases, and the analysis results are consistent with the theory of Traditional Chinese Medicine.

Highlights

Clustering belongs to unsupervised learning, so it can improve the objectivity of the results when applied to medical research. e earliest application of clustering technology to assist medical diagnosis was in the 1970s [1]
Xu et al simulated the process of Traditional Chinese Medicine (TCM) diagnosis and created an online analysis platform for TCM based on Latent Tree to assist TCM diagnosis
In the K-means clustering algorithm, it is necessary to determine the number of clusters K in advance based on experience and randomly select the initial clustering center. erefore, the results of cluster analysis are greatly affected by the selection of initial clustering center, outliers, and noise data, which will lead to the unstable results and fall into local optimal solution

Summary

Introduction

Clustering belongs to unsupervised learning, so it can improve the objectivity of the results when applied to medical research. e earliest application of clustering technology to assist medical diagnosis was in the 1970s [1]. Is paper proposes an improved mutation strategy of DE and optimizes the determination problem of K-Means clustering center, which can replace the traditional K-Means clustering algorithm to update the clustering center continuously. (1) An improved DE clustering algorithm is proposed for analyzing the data of Traditional Chinese Medicine (2) Experimental studies are used, using UCI standard datasets to verify the performance of the proposed algorithm e rest of this paper is organized as follows: Section 2 introduces the relevant theories. K-Means algorithm belongs to hard clustering algorithm, which is a prototype-based objective function clustering method It obtains the optimized objective function by calculating the distance from data points to the prototype and obtains the adjustment rules of iterative operation by using the function to calculate the extreme value.

Improved Differential Evolution-Based KMeans Clustering Algorithm

Population Diversity-Based Double-Mutation Operation

Simulation Experiment and Analysis

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