Kernel k-means Algorithm

Kernel k-means:

Kernel k-means clustering method is a non-linear extension of the conventional k-means clustering method. Girolami (2002) first proposed the kernel k-means clustering method. It is an iterative method. It first maps the data points from the input space to a higher dimensional feature space through a nonlinear transformation and then minimizes the clustering error in that feature space.

Kernel k-means Algorithm:

This algorithm applies the same trick as k-means but with one difference here in the calculation of distance. The kernel method is used instead of the Euclidean distance.

1. For each cluster Cj, find |Cj| and G(Cj).

2. Compute F(Xi, Cj) for each Xi and for each cluster Cj.

3. Find ||Φ(Xi) – μj||2 using equation(4) and assign Xi to its nearest center.

4. Update μj, for j=1 to k, using equation(2).

5. Repeat step-1 through step-4 till convergence.