Introduction - If you have any usage issues, please Google them yourself
In recent years, spectral clustering has become one of the most popular modern clustering
algorithms. It is simple to implement, can be solved eciently by standard linear algebra software,
and very often outperforms traditional clustering algorithms such as the k-means algorithm. On
the rst glance spectral clustering appears slightly mysterious, and it is not obvious to see why
it works at all and what it really does. The goal of this tutorial is to give some intuition on
those questions. We describe di erent graph Laplacians and their basic properties, present the
most common spectral clustering algorithms, and derive those algorithms from scratch by several
di erent approaches. Advantages and disadvantages of the di erent spectral clustering algorithms
are discussed.