一种用于中医四诊分析的子空间聚类方法

计算机与现代化 ›› 2020, Vol. 0 ›› Issue (12): 67-71.

一种用于中医四诊分析的子空间聚类方法

(1.常州市中医医院,江苏常州213003；2.金陵科技学院网络与通信工程学院,江苏南京211169；
3.安徽理工大学计算机科学与工程学院,安徽淮南232001）

出版日期:2021-01-07 发布日期:2021-01-07
作者简介:许立辉（1977—），男，江苏常州人，工程师，硕士，研究方向：信息管理，数据挖掘，中医病证分析，E-mail： lwtg_xlh@126.com；陈敏(1995—)，男，江苏淮安人，硕士研究生,研究方向：人工智能，大数据技术，E-mail： 842679178@qq.com；王池社（1974—），男，教授，博士，研究方向：大数据技术，人工智能，机器学习，E-mail： wyxcs@163.com。
基金资助:
国家自然科学基金资助项目（61375121）

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

(1. Changzhou Hospital of Traditional Chinese Medicine, Changzhou 213003, China;
2. College of Network and Communication Engineering, Jinling Institute of Technology, Nanjing 211169, China;
3. College of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China)

Online:2021-01-07 Published:2021-01-07

摘要/Abstract

摘要： 中医四诊分析是基于四诊信息进行中医证候分类研究的重要内容，构建有效的中医四诊分析模型可以更好地挖掘中医证候间的关联关系，从而为中医临床提供决策支持。本文通过对子空间聚类CLIQUE算法的分析，结合四诊信息的数据特征，提出一种基于限定空间搜索策略的改进CLIQUE算法（ChM-CLIQUE）。通过优化CLIQUE算法的搜索策略，以稠密单元中网格密度最大的单元为中心进行深度优先搜索生成聚类簇，提高算法的性能，同时基于聚类簇中样本高斯分布的特性引入网格自适应密度，增强聚类边界的识别精度。在中医临床采集的数据集上进行多组对比实验，实验结果表明本文算法的轮廓系数较CLIQUE算法有显著性的提高。

关键词: 四诊信息, 子空间聚类, CLIQUE算法

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

许立辉, 陈敏, 王池社. 一种用于中医四诊分析的子空间聚类方法[J]. 计算机与现代化, 2020, 0(12): 67-71.

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.

参考文献［23］

［1］	吕爱平. 疾病证候分类研究——以类风湿关节炎为例［J］. 中国中西医结合杂志, 2019,39(2):136-139.
［2］	刘丽蓉,詹秀菊. 基于BP神经网络和支持向量机的荨麻疹证候分类探讨［J］. 广州中医药大学学报, 2020,37(3):573-577.
［3］	齐婧,郑刚,王永刚,等. 基于聚类分析的冠心病心绞痛中医证候特点研究［J］. 现代中西医结合杂志, 2020,29(4):351-354.
［4］	VIDAL R. Subspace clustering［J］. IEEE Signal Processing Magazine, 2011,28(2):52-68.
［5］	HU J H, PEI J. Subspace multi-clustering: A review［J］. Knowledge and Information Systems, 2018,56(2):257-284.
［6］	GENG Y L A, LI Q Y, LIANG M F, et al. Local-density subspace distributed clustering for high-dimensional data［J］. IEEE Transactions on Parallel and Distributed Systems, 2020,31(8):1799-1814.
［7］	ZHONG G, PUN C M. Nonnegative self-representation with a fixed rank constraint for subspace clustering［J］. Information Sciences, 2020,518:127-141.
［8］	LAKSHMI B J, SHASHI M, MADHURI K B. A rough set based subspace clustering technique for high dimensional data［J］. Journal of King Saud University-Computer and Information Sciences, 2020,32(3):329-334.
［9］	XUE X Q, ZHANG X Q, FENG X H, et al. Robust subspace clustering based on non-convex low-rank approximation and adaptive kernel［J］. Information Sciences, 2020,513:190-205.
［10］	WANG X D, CHEN R C, YAN F, et al. Fast adaptive k-means subspace clustering for high-dimensional data［J］. IEEE Access, 2019,7:42639-42651.
［11］	LIU C, XIE J, ZHAO Q, et al. Novel evolutionary multi-objective soft subspace clustering algorithm for credit risk assessment［J］. Expert Systems with Applications, 2019,138:DOI:10.1016/j.eswa.2019.112827.
［12］	GUO Y, LI H R, CAI M L, et al. Integrative subspace clustering by common and specific decomposition for applications on cancer subtype identifycation［J］. BMC Medical Genomics, 2019,12(S9):DOI:10.1186/s12920-019-0633-1.
［13］	ZHOU Z H, TIAN B. Research on community detection of online social network members based on the sparse subspace clustering approach［J］. Future Internet, 2019,11(12):DOI: 10.3390/fi11120254.
［14］	ZHANG K N, CHEN Y, NIE Y. Hunting image: Taxi search strategy recognition using sparse subspace clustering［J］. Transportation Research Part C—Emerging Technologies, 2019,109:250-266.
［15］	ABDOLALI M, RAHMATI M. Robust subspace clustering for image data using clean dictionary estimation and group lasso based matrix completion［J］. Journal of Visual Communication and Image Representation, 2019,61:303-314.
［16］	万静,郑龙君,何云斌,等. 高维不确定数据的子空间聚类算法［J］. 计算机应用, 2019,39(11):3280-3287.
［17］	李世明,张秉桢,杜军,等. 一种用于居住热区聚类的改进CLIQUE算法［J］. 小型微型计算机系统, 2020,41(1):61-65.
［18］	张文博,侯晓荣. 基于高斯分布的大气光估计算法［J］. 计算机科学, 2018,45(4):301-305.
［19］	张宜浩,刘智,朱常鹏. 融合距离度量和高斯混合模型的中文词义归纳模型［J］. 计算机科学, 2017,44(8):265-269.
［20］	张士豪,顾益军,张俊豪. 微博自动分类系统设计［J］. 信息网络安全, 2016(1):81-87.
［21］	杨光. 面向高维数据的k近邻查询算法研究［D］. 哈尔滨:哈尔滨工业大学, 2018.
［22］	段峰峰,王永滨,杨丽芳,等. 基于主成分分析方向深度梯度直方图的立体视觉深度图特征提取［J］. 计算机应用, 2016,36(1):222-226.
［23］	谢娟英,周颖,王明钊,等. 聚类有效性评价新指标［J］. 智能系统学报, 2017,12(6):873-882.