There has been a great deal of attention in recent times particularly in machine learning to representation of multivariate data points x by K(x, ·) where K is positive and symmetric and thus induces a reproducing kernel Hilbert space.The idea is then to use the matrix

and other inference.(Jordan and Fukumizu(2006) for instance. Nadler et. al(2006) connected this approach to one based on random walks and diffusion limits and indicated a connection to kernel density estimation.By making at least a formal connection to a multiplication operator on a function space we make further connection and show how clustering results of Beylkin ,Shih and Yu (2008) which apparently differ from Nadler et al. can be explained.