Research Output
A new biclustering technique based on crossing minimization
  Clustering only the records in a database (or data matrix) gives a global view of the data. For a detailed analysis or a local view, biclustering or co-clustering is required, involving the clustering of the records and the attributes simultaneously. In this paper, a new graph-drawing-based biclustering technique is proposed based on the crossing minimization paradigm that is shown to work for asymmetric overlapping biclusters in the presence of noise. Both simulated and real world data sets are used to demonstrate the superior performance of the new technique compared with two other conventional biclustering approaches.

  • Type:

    Article

  • Date:

    06 July 2006

  • Publication Status:

    Published

  • DOI:

    10.1016/j.neucom.2006.02.018

  • ISSN:

    0925-2312

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006.312 Data mining

  • Funders:

    Historic Funder (pre-Worktribe)

Citation

Abdullah, A., & Hussain, A. (2006). A new biclustering technique based on crossing minimization. Neurocomputing, 69(16-18), 1882-1896. https://doi.org/10.1016/j.neucom.2006.02.018

Authors

Keywords

Knowledge discovery; Data mining; Biclustering; Co-clustering; Graph drawing; Crossing minimization; Overlapping biclusters; Noise

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