Data Mining Technology In The Celandine Alkaloid Content Change Rule Research Applications

Data mining technology as a kind of applied to find the hidden data regularity of advanced data processing technology,involves statistics,database,machine learning and so on,because of its strong ability and application field,and get broad attention from all walks of life,has gradually become an important research field of people.Along with the economic and social development,data mining technology has penetrated into various fields,through the analysis of massive data mining,and find effective rules and provide valuable results for further research.This paper is using the application of data excavate technology in the celandine alkaloid content change rule research and analysis.Celandine is belonging to poppy category,have clear heat pain,cough,antiviral,detoxification cure boils,the effect of detumescence,growth in the valley of the wet area,forest edge gutter,widely distributed in China mainly distributed in the northeast,and many other provinces,extremely rich in natural resources.Celandine have medicinal effect of alkaloids are the main composition of the material,contains celandine alkali,6 methoxy 2 hydrogen celandine alkali,rhizoma coptidis alkaloid,two hydrogen sanguinarine,6-methoxy two hydrogen sanguinarine.Celandine has very rich medicinal value.Thus for Celandine alkaloid content change rule of data mining,in order to fully develop and utilize celandine this plant resources,Determine the appropriate harvest time,extracting celandine alkaloids,as well as the correct evaluation of many place of celandine quality,for exploring celandine alkaloids research provide more ideas.Hope to helpful for the way of celandine sampling,extraction,storage,etc.In this paper,through the analysis of sample data of chelidonic different picking periods,different regions,using the association rules algorithm and clustering algorithm,established the application changes of celandine alkaloids content in data mining technology.Through the research on fuzzy C mean clustering algorithm and the Apriori theory,the design is suitable to chelidonic alkaloids data algorithm.Fuzzy C mean clustering and Apriori algorithm is applied to the greater celandine alkaloids data.First,select the data source,the research objects are on May — October of celandine alkaloids data,from jilin,liaoning,chengde,and Beijing,then data preprocessing.Second,carries on fuzzy C means clustering analysis,underground part and ground part,different part,clustering results are analyzed;After the analysis of the clustering results,In order to further determine the credibility of the clustering results and determine the relationship between the underground and ground part chelidonic in different periods,and then apply the Apriori algorithm to obtain correlation analysis,analysis of applied value,find out the factors influencing the changes of content of alkaloids in Chelidonium.By the method of cluster analysis and association rules combined with the method,so as to obtain the best quality varieties of alkaloids in Chelidonium samples in different harvest periods and different habitats,which start to provide more feasible theoretical basis for the development and provide the basis for the selection of excellent chelidonic varieties.