  An incremental approach for computation of convex hull for data points in
two-dimensions is presented. The algorithm is not output-sensitive and costs a
time that is linear in the size of data points at input. Graham's scan is
applied only on a subset of the data points, represented at the extremal of the
dataset. Points are classified for extremal, in proportion with the modular
distance, about an imaginary point interior to the region bounded by convex
hull of the dataset assumed for origin or center in polar coordinate. A subset
of the data is arrived by terminating at until an event of no change in maximal
points is observed per bin, for iteratively and exponentially decreasing
intervals.
