Phylogenetic Tree Construction for DNA Sequences using Clustering Methods

AbstractA phylogenetic tree or an evolutionary tree is a graph that shows the evolutionary relationships among various biological species based on their genetic closeness. In the proposed model, initially individual samples are selected and a matrix is generated which shows the genetic distances between individuals. Then by using the distance matrix, samples are divided into clusters. Then phylogenetic trees for each cluster are constructed independently. In order to find the clustering algorithm that gives the most effective clusters for biological data are k-means algorithm, k-medoid algorithm and density-based algorithms are used. By doing a comparative study it is concluded that density-based clustering (DBSCAN) is quite good for biological dataset because; this algorithm performs efficiently for low dimensional data and the algorithm is robust towards outliers and noise points. The phylogenetic tree for every individual clusters are formed and finally joined to create the final phylogenetic tree. From the experimental evaluation it has been found that, the DBSCAN is showing better result showing appropriate information and it is faster than other two methods