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An Exchange Algorithm for Two-Mode Cluster Analysis

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Innovations in Classification, Data Science, and Information Systems

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Abstract

A comprehensive simulation study recently has shown that, in order to identify best two-mode classifications, the user may apply different algorithms and select the result yielding the lowest squared centroid distance measurement (SCD). Knowing the outperformer among several goodness-of-fit measures creates the premises to develop an exchange algorithm for two-mode classifications. This paper presents the algorithm and discusses significance in gain of precision based on a large Monte Carlo Simulation study.

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Author information

Authors and Affiliations

  1. Institute for Corporate Development and Organization, Munich School of Management, Ludwig-Maximilians-University, D-80539, Munich, Germany

    Manfred Schwaiger

  2. Accenture GmbH, Maximilianstr. 35, D-80539, Munich, Germany

    Raimund Rix

Authors
  1. Manfred Schwaiger
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  2. Raimund Rix
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Editor information

Editors and Affiliations

  1. Institute of Business Administration and Economics, Brandenburg University of Technology Cottbus, Konrad-Wachsmann-Allee 1, 03046, Cottbus, Germany

    Daniel Baier (Chair of Marketing and Innovation Management) (Chair of Marketing and Innovation Management)

  2. Department of Medical Biometrics Charité Virchow-Klinikum, Humboldt University Berlin, 13344, Berlin, Germany

    Klaus-Dieter Wernecke

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© 2005 Springer-Verlag Berlin · Heidelberg

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Schwaiger, M., Rix, R. (2005). An Exchange Algorithm for Two-Mode Cluster Analysis. In: Baier, D., Wernecke, KD. (eds) Innovations in Classification, Data Science, and Information Systems. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-26981-9_8

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