Self-Organizing Maps for Structured Domains: Theory, Models, and Learning of Kernels

  • Fabio Aiolli
  • Giovanni Da San Martino
  • Markus Hagenbuchner
  • Alessandro Sperduti
Part of the Studies in Computational Intelligence book series (SCI, volume 247)

Introduction

Self-Organizing Maps (SOMs) are a form of Machine Learning methods which are popularly applied as a tool to either cluster vectorial information, or to produce a topology preserving projection of high dimensional data vectors onto a low dimensional (often 2-dimensional) display space [20]. A SOM is generally trained unsupervised. The computational complexity of the underlying algorithms grows linearly with the size and number of inputs, which renders the SOM suitable for data mining tasks. The standard SOM algorithm is defined on input domains involving fixed sized data vectors. It is however recognized that many problem domains are naturally represented by structured data which are more complex than fixed sized vectors. Just to give some examples, in speech recognition, data is available in the form of variable length temporal vectors, while in Chemistry data is most naturally represented through molecular graphs.Moreover, numerous data mining tasks provide structural information which may be important to consider during the processing. For example, document mining in the world wide web involves both inter-document structure due to the formatting or hypertext structure, and intra-document structure due to hyperlink or reference dependencies. Note that any model capable of dealing with graphs can be used also in applications involving vectors, sequences, and trees, since these are special cases of graphs.

Keywords

Kernel Function Root Node Leaf Node Data Label Winning Neuron 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Fabio Aiolli
    • 1
  • Giovanni Da San Martino
    • 1
  • Markus Hagenbuchner
    • 2
  • Alessandro Sperduti
    • 1
  1. 1.Dept. of Pure and Applied MathematicsPadua UniversityPaduaItaly
  2. 2.School of Computer Science and Software Engineering, Faculty of InformaticsUniversity of WollongongWollongongAustralia

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