confusing regarding to kmeans clulstering for data correlation
You will not get what you seek this way, but you are on the right path. Use the correlation between two instruments as a measure of similarity, and then perform spectral clustering with this measure as the kernel.

Basically, you will start with your correlation matrix $R$, and build the corresponding Laplacian matrix $L$. The eigen vectors corresponding to the smallest eigen values of $L$ will give you a projection space in which you can perform k-means clustering.

This technique is efficient if you have a good similarity measure, but only works for reasonably sized datasets (because you need the eigen decomposition of the $n \times n$ matrix).