  One of the popular measures of central tendency that provides better
representation and interesting insights of the data compared to the other
measures like mean and median is the metric mode. If the analytical form of the
density function is known, mode is an argument of the maximum value of the
density function and one can apply the optimization techniques to find mode. In
many of the practical applications, the analytical form of the density is not
known and only the samples from the distribution are available. Most of the
techniques proposed in the literature for estimating the mode from the samples
assume that all the samples are available beforehand. Moreover, some of the
techniques employ computationally expensive operations like sorting. In this
work we provide a computationally effective, on-line iterative algorithm that
estimates the mode of a unimodal smooth density given only the samples
generated from the density. Asymptotic convergence of the proposed algorithm
using an ordinary differential equation (ODE) based analysis is provided. We
also prove the stability of estimates by utilizing the concept of
regularization. Experimental results further demonstrate the effectiveness of
the proposed algorithm.
