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Comparing Biclustering Algorithms

This study presents a detailed comparison of three prominent biclustering algorithms: Statistical Algorithmic Method for Bicluster Analysis (SAMBA), Order-Preserving Submatrix (OPSM), and Flexible Overlapped Clustering (FLOC). Each algorithm's strengths and weaknesses are examined through various criteria including user-defined parameters, coverage, and precision. The application of these methods on different datasets shows significant differences in clustering results, particularly in terms of clarity and overlap. This analysis aids in selecting the most suitable method for specific biclustering needs in data science.

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Comparing Biclustering Algorithms

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  1. Comparing Biclustering Algorithms • Todd A. Gibson • University of Colorado Health Sciences Center • Todd.Gibson@UCHSC.edu

  2. Algorithms Statistical Algorithmic Method for Bicluster Analysis (SAMBA). Order-Preserving Submatrix (OPSM). Flexible Overlapped Clustering (FLOC).

  3. Method User-Defined Parameters Coverage Precision Focus SAMBA (graph) - High Low Blurry OPSM (ranks) # Columns High Med Sharp FLOC (residue) # Clusters Low High Sharp Results

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