  Given a n points in two dimensional space, a Manhattan Network G is a network
that connects all n points with either horizontal or vertical edges, with the
property that for any two point in G should be connected by a Manhattan path
and distance between this two points is equal to Manhattan Distance. The
Minimum Manhattan Network problem is to find a Manhattan network with minimum
network length, i.e., summation of all line segment in network should be
minimize. In this paper, we proposed a 2-approximation algorithm with time
complexity O(|E|lgN) where |E| is the number of edges and N is the number of
nodes. Using randomly generated datasets, we compare our result with the
optimal one.
