I am working with cyclical data (Days 1-7, hours 1-24). I want to project it into a feature space that can understand that 1 and 7 are close days and 1 and 24 are closer than 22 and 24, etc, and then I will do k-means clustering using kkmeans in R.
Does a Guassian RBF accomplish this? I suspect not. If not, can someone help me think of a kernel, preferably one from this list (w/ modified parameters possibly) so I do not have to reprogram it?
rbfdot Radial Basis kernel "Gaussian" polydot Polynomial kernel vanilladot Linear kernel tanhdot Hyperbolic tangent kernel laplacedot Laplacian kernel besseldot Bessel kernel anovadot ANOVA RBF kernel splinedot Spline kernel stringdot String kernel
It seems like it might be something like (for days)
d(x,y) = min(d(x,y),d(x+7,y))
Is this possible to program into k-means package you think? It seems difficult to modify as a lot is written in FORTRAN