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      A Tutorial on Spectral Clustering

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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. On the first 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 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.

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          Journal
          01 November 2007
          Article
          0711.0189
          84be95f0-11e2-4865-b37f-2c66400c0a26
          History
          Custom metadata
          Statistics and Computing 17(4), 2007
          cs.DS cs.LG

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