Complex Systems Entropy Cluster Algorithm and Its Application in Stroke Clinics

Using unsupervised algorithms to cluster four diagnosis information data is mainstream and difficulty of Traditional Chinese Medicine clinical research.Based on ischemic stroke clinical data collected,an unsupervised complex system entropy cluster algorithm was proposed.The algorithm ameliorates the traditional correlation coefficient,it not only can realize unsupervised cluster,but also can realize that a variable appears in different clusters.An N-class correlation concept was proposed and proved to significantly accelerate the convergence time of the algorithm.The algorithm was applied to the clinical data of stroke,extracting the hackneyed syndromes in the Stroke unsupervisedly.The results accord to clinics significantly.Finally,the supervised part of data was taken into account to validate the algorithm,reaching a sensitivity of 97.3%.In a word,the algorithm paces a benign mathematical and physical base for standardization of healing stroke by Chinese medicine.