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Abstract:
In recent years, spectral clustering has become one of the most
popular modern clustering algorithms. It is simple to implement, can
be solved efficiently by standard linear algebra software, and very
often outperforms traditional clustering algorithms such as the
k-means algorithm. Nevertheless, on the first glance spectral
clustering looks a bit mysterious, and it is not obvious to see why it
works at all and what it really does. This article is a tutorial
introduction to spectral clustering. We describe different graph
Laplacians and their basic properties, present the most common
spectral clustering algorithms, and derive those algorithms from
scratch by several different approaches. Advantages and disadvantages
of the different spectral clustering algorithms are discussed.
