I want to cluster a dataset using spectral clustering. Assuming $X$ is $d \times n$ data matrix as $n$ is the number of data samples. I construct a directed Adjacency matrix $W, n \times n$ in which each row $i$ relates $x_i$ to other data points with positive weights. Then using the following Adjacency matrix with "Spectral Clustering".
I compare the result with the "Spectral Clustering" when using RBF similarity kernel ($K$), the above $A$ matrix brings better results than RBF kernel (40% comparing to 58%)
But, if i use that RBF-kernel ($K$) with the kernel kmeans algorithm i achieve much better accuracy, around 90%!
So does it mean that kernel k-means is a better clustering approach for this case? or maybe i used the spectral clustering in a wrong way? BTW, i already tried "symmetric/asymmetric normalized" and "unormalized" Laplacian matrices for "Spectral Clustering" and the best is with the "unormalized" case.