Classification of large datasets is an important data mining problem. Many classification algorithms have been proposed in the literature, but studies have shown that so far no algorithm uniformly outperforms all other algorithms in terms of quality. In this process, we present a unifying framework for decision tree classifiers that separates the depth first search algorithm for constructing a decision tree from the central features that determine the quality of the tree. In the existing system, the tree construction is not feasible for search the phylogeny corresponding to the taxa. In the proposed, we have constructed the phylogeny tree depending on the dataset values. The decision tree is built using the depth first search algorithm. In the tree we have find the position of the tree and the details of the node also.