Numerical Data Processing by The Implementation of Trees and Graphs

Abstract

Trees and Graphs play a vital role in transport and logistics. In tree, decision tree is one of the important, not only implemented for data processing, but also considered for Numerical data analysis. The decision tree is a flow chart-like structure, in which each internal node represents a ‘test’ on an attribute, which has a node known as root being at the top, which further divides the given data into branches depending upon the conditions. Every branch consists of a rule, and each leaf node is its outcome. A support tool with a tree-like structure that models probable outcomes, cost of resources, utilities, and possible consequences. Decision trees are also used in operations research along with planning logistics. They can help in determining appropriate plans that will help a company achieve its target. In Graph, Dijkstra's algorithm is an admired algorithm used to find the shortest paths between nodes in a graph, which may represent road networks for example. Dijkstra's algorithm finds the shortest path from a given source node to every other node. It can also be used to find the shortest path to a specific destination node, by concluding the algorithm once the shortest path to the destination node is found. It is also commonly used on graphs where the edge weights are in real numbers.  A common application of shortest path algorithms is network routing protocols and also support in route optimization for delivery services, ensuring timely and cost-effective deliveries by finding the best paths for transportation.