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Biclustering methods for one-mode asymmetric matrices.

Michael J Brusco1,2, Patrick Doreian3,4, Douglas Steinley5

  • 1Florida State University, Tallahassee, FL, USA. mbrusco@fsu.edu.

Behavior Research Methods
|April 23, 2015
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Summary
This summary is machine-generated.

This study introduces biclustering for one-mode matrices, enabling separate object partitions for rows and columns. This method is valuable for analyzing asymmetric data in psychology and other fields.

Keywords:
BiclusteringClusteringNonnegative matrix factorizationOne-mode asymmetric matrixTwo-mode KL-means partitioningTwo-mode blockmodeling

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Area of Science:

  • Psychology
  • Data Analysis
  • Computational Methods

Background:

  • Asymmetric one-mode matrices present objects with differing row/column roles.
  • Traditional partitioning methods are restrictive for such data.
  • Applications span experimental psychology, social sciences, and bibliometrics.

Purpose of the Study:

  • To address the limitations of traditional partitioning for asymmetric one-mode matrices.
  • To introduce and evaluate biclustering approaches for simultaneous row and column object partitioning.
  • To provide practical tools for analyzing complex psychological and interdisciplinary data.

Main Methods:

  • Developed and compared several biclustering algorithms.
  • Applied methods to asymmetric one-mode data matrices.
  • Utilized datasets from empirical psychological literature.

Main Results:

  • Demonstrated the effectiveness of biclustering in establishing separate row and column partitions.
  • Showcased the flexibility of biclustering for diverse applications.
  • Provided MATLAB m-files for implementation.

Conclusions:

  • Biclustering offers a more flexible and powerful approach for analyzing asymmetric one-mode data.
  • The proposed methods are applicable across various psychological subfields and beyond.
  • The availability of code facilitates further research and application.