COMPACT: A Comparative Package for Clustering Assessment

  • Roy Varshavsky
  • Michal Linial
  • David Horn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3759)


There exist numerous algorithms that cluster data-points from large-scale genomic experiments such as sequencing, gene-expression and proteomics. Such algorithms may employ distinct principles, and lead to different performance and results. The appropriate choice of a clustering method is a significant and often overlooked aspect in extracting information from large-scale datasets. Evidently, such choice may significantly influence the biological interpretation of the data. We present an easy-to-use and intuitive tool that compares some clustering methods within the same framework. The interface is named COMPACT for Comparative-Package-for-Clustering-Assessment. COMPACT first reduces the dataset’s dimensionality using the Singular Value Decomposition (SVD) method, and only then employs various clustering techniques. Besides its simplicity, and its ability to perform well on high-dimensional data, it provides visualization tools for evaluating the results. COMPACT was tested on a variety of datasets, from classical benchmarks to large-scale gene-expression experiments. COMPACT is configurable and expendable to newly added algorithms.


Cluster Algorithm Singular Value Decomposition Yeast Cell Cycle Quantum Cluster Real Classification 
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 2005

Authors and Affiliations

  • Roy Varshavsky
    • 1
  • Michal Linial
    • 2
  • David Horn
    • 3
  1. 1.School of Computer Science and EngineeringThe Hebrew University of JerusalemIsrael
  2. 2.Dept of Biological Chemistry, Institute of Life SciencesThe Hebrew University of JerusalemIsrael
  3. 3.School of Physics and AstronomyTel Aviv UniversityIsrael

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