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International Workshop on Machine Learning, Optimization and Big Data

MOD 2015: Machine Learning, Optimization, and Big Data pp 318–324Cite as

A Parallel Consensus Clustering Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9432))

Abstract

Consensus clustering is a stability-based algorithm with a prediction power far better than other internal measures. Unfortunately, this method is reported to be slow in terms of time and hard to scalability. We presented here consensus clustering algorithm optimized for multi-core processors. We showed that it is possible to obtain scalable performance of the compute-intensive algorithm for high-dimensional data such as gene expression microarrays.

The research is financed by Wroclaw University of Technology statutory grant.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Electronics, Wroclaw University of Technology, Wyb. Wyspianskiego 25, 50-370, Wroclaw, Poland

    Olgierd Unold & Tadeusz Tagowski

Authors
  1. Olgierd Unold
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  2. Tadeusz Tagowski
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Corresponding author

Correspondence to Olgierd Unold .

Editor information

Editors and Affiliations

  1. University of Florida, Gainsville, Florida, USA

    Panos Pardalos

  2. University of Catania, Catania, Italy

    Mario Pavone

  3. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  4. University of Catania, Catania, Italy

    Vincenzo Cutello

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© 2015 Springer International Publishing Switzerland

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Unold, O., Tagowski, T. (2015). A Parallel Consensus Clustering Algorithm. In: Pardalos, P., Pavone, M., Farinella, G., Cutello, V. (eds) Machine Learning, Optimization, and Big Data. MOD 2015. Lecture Notes in Computer Science(), vol 9432. Springer, Cham. https://doi.org/10.1007/978-3-319-27926-8_28

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  • DOI: https://doi.org/10.1007/978-3-319-27926-8_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27925-1

  • Online ISBN: 978-3-319-27926-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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