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International Symposium on Intelligent Data Analysis

IDA 1997: Advances in Intelligent Data Analysis Reasoning about Data pp 137–148Cite as

A connectionist approach to the distance-based analysis of relational data

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1280))

Abstract

Objects with higher structural complexity often cannot be described by feature vectors without losing important structural information. Several types of structured objects can be represented adequately by labeled graphs. The similarity of such descriptions is difficult to define and to compute. However, many algorithms in machine learning, knowledge discovery, pattern recognition and classification are based on the estimation of the similarity between the analysed objects. In order to make similarity based algorithms like nearest neighbor classifiers, clustering, or generalised prototype learning accessible for the analysis of relational data, a connectionist approach for the determination of the similarity of arbitrary labeled graphs is introduced.

Using an example from organic chemistry, it is shown that classifiers based on the connectionist approach to structural similarity to be considered in this paper perform very satisfactorily in comparison with recent logical and feature vector approaches. Moreover, being able to handle relational data in a natural way without any loss of structural information, the algorithms need only a subset of the given features of the objects for classification.

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Authors and Affiliations

  1. Technical University of Berlin, FR 5-8, Franklinstr. 28/29, D-10587, Berlin, Germany

    Kristina Schädler & Fritz Wysotzki

Authors
  1. Kristina Schädler
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  2. Fritz Wysotzki
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Xiaohui Liu Paul Cohen Michael Berthold

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© 1997 Springer-Verlag

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Schädler, K., Wysotzki, F. (1997). A connectionist approach to the distance-based analysis of relational data. In: Liu, X., Cohen, P., Berthold, M. (eds) Advances in Intelligent Data Analysis Reasoning about Data. IDA 1997. Lecture Notes in Computer Science, vol 1280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0052836

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  • DOI: https://doi.org/10.1007/BFb0052836

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63346-4

  • Online ISBN: 978-3-540-69520-2

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