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Part of the book series: Studies in Computational Intelligence ((SCI,volume 249))

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Different Similarity Graphs (or Connectivity Graphs)

To construct a similarity graph we transform a given set x1,...,x n of data points with pairwise similarities s ij or distances d ij into a graph. There are several popular methods to construct similarity graphs [154]. The goal of constructing similarity graphs is to model the local neighborhood relationships between data points. In this section we review two popular methods to construct a similarity graph, and then we introduce a new algorithm that solves some problems that can not be solved by the others.

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© 2009 Springer-Verlag Berlin Heidelberg

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Barbakh, W.A., Wu, Y., Fyfe, C. (2009). Connectivity Graphs and Clustering with Similarity Functions. In: Non-Standard Parameter Adaptation for Exploratory Data Analysis. Studies in Computational Intelligence, vol 249. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04005-4_7

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  • DOI: https://doi.org/10.1007/978-3-642-04005-4_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04004-7

  • Online ISBN: 978-3-642-04005-4

  • eBook Packages: EngineeringEngineering (R0)

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