Classification and Seriation by Iterative Reordering of a Data Matrix

  • Richard Streng
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


A heuristic algorithm is presented which searches for the reordering of rows and columns of a symmetric similarity matrix in order to fulfill, at least approximatively, the Robinson condition. The algorithm uses pairwise interchanges in constructive and iterative strategies. — A rectangular m×n matrix of two different sets of parameters can be treated by first converting or preprocessing the data into two square similarity matrices, each for rows and columns, before applying the above mentioned technique. The resulting orderings for rows and columns in the m×n matrix yields a pattern whose underlying structure can be interpreted by inspection. — Agglomerative hierarchical classification can be obtained after the rearrangement using only neighbouring objects (rows, columns). — A computer program has been implemented with a fast reordering algorithm and a graphical dendrogram presentation.1


Similarity Matrix Main Diagonal Knowledge Organization Neighbouring Classis Rectangular Matrix 
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 1991

Authors and Affiliations

  • Richard Streng
    • 1
  1. 1.Institute for ZoologyUniversity of RegensburgRegensburgGermany

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