Application Of Hierarchical Clustering Algorithm Based On Interaction To Gene Flow In Ageratina Adenophora

There is a gene exchange between the same or different species of biological species,which refers to the process of the flow of genetic information of a biological population from the population to other biological populations.Gene flow can reveal the genetic relationship between biological populations,calculate the propagation speed of the population,the distance of transmission,the settlement process and the future trend of population forecasting.At present,in the field of biology research on gene flow is relatively simple.Structure model clustering is one of the most frequently used research methods in this field.This method is a clustering algorithm based on K-means algorithm.Before the cluster starts,calculate ?K to estimate the number of clusters.However,in practical applications,the value of ?K and the results of clustering are often different.In the analysis of the results of clustering,and can not guarantee that the clustering results in the field of biological expression is accurate and reasonable.Therefore,this paper proposes a hierarchical clustering algorithm based on interaction and is applied to the study of gene flow,and compared with the structure model clustering algorithm to analyze the advantages of the two algorithms.Eupatorium adenophorum is a highly harmful malignant weed,belonging to invasive organisms.The 20th century,40 years by the Sino-Burmese border into southern China's Yunnan.Eupatorium adenophorum invasion of the trend and the increasingly serious harm,seriously affecting and destroying the local human and animal health and agriculture and forestry,animal husbandry development.Yunnan Province,the total area of harm has reached 64,000 km2,to the southwest region has brought huge economic losses.The genetic loci of 12 female and progeny populations of Eupatorium adenophorum in the northern section of Nujiang River in Yunnan were selected as data samples.Using the hierarchical clustering algorithm based on interaction and the clustering algorithm with the highest frequency in the field of biology,the hybrid population of progeny population,female parent population and progeny and female parent were clustered respectively.The clustering results show that the clustering results of the two kinds of clustering algorithms are consistent with the hybrid population of the female parent and the parent and the female parent,and the results of the two clustering algorithms are different for the offspring population.In order to verify which results are more reasonable in the field of biology,the concept of genetic distance in biology is introduced as a measure.The genetic distance is the amount that reflects the degree of differentiation and variation between biological populations.Clustering analysis of the genetic distance of the offspring population revealed that the results obtained by cluster clustering algorithm based on interaction were consistent with those obtained by genetic distance analysis.Therefore,it is concluded that the hierarchical clustering algorithm based on interaction is more reasonable and the description is more accurate in the field of biology.