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Semantic Distillation: A Method for Clustering Objects by their Contextual Specificity

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Nature Inspired Cooperative Strategies for Optimization (NICSO 2007)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 129))

Abstract

Techniques for data-mining, latent semantic analysis, contextual search of databases, etc. have long ago been developed by computer scientists working on information retrieval (IR). Experimental scientists, from all disciplines, having to analyse large collections of raw experimental data (astronomical, physical, biological, etc.) have developed powerful methods for their statistical analysis and for clustering, categorising, and classifying objects. Finally, physicists have developed a theory of quantum measurement, unifying the logical, algebraic, and probabilistic aspects of queries into a single formalism.

The purpose of this paper is twofold: first to show that when formulated at an abstract level, problems from IR, from statistical data analysis, and from physical measurement theories are very similar and hence can profitably be cross-fertilised, and, secondly, to propose a novel method of fuzzy hierarchical clustering, termed semantic distillation — strongly inspired from the theory of quantum measurement —, we developed to analyse raw data coming from various types of experiments on DNA arrays. We illustrate the method by analysing DNA arrays experiments and clustering the genes of the array according to their specificity.

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Author information

Authors and Affiliations

  1. Institut de recherche mathématique (UMR6625), Université de Rennes 1, Italy

    Thomas Sierocinski & Dimitri Petritis

  2. INSERM U620, Université de Rennes 1, Italy

    Antony Le Béchec & Nathalie Théret

Authors
  1. Thomas Sierocinski
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  2. Antony Le Béchec
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  3. Nathalie Théret
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  4. Dimitri Petritis
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Editor information

Editors and Affiliations

  1. School of Computer Sciences and Information Technology, University of Nottingham, Jubilee Campus, Nottingham, NG81BB, UK

    Natalio Krasnogor

  2. Department of Mathematics and Computer Science, University of Catania, v.le A. Doria, 6, 95125, Catania, Italy

    Giuseppe Nicosia  & Mario Pavone  & 

  3. Department of Computer Science and Artificial Intelligence E.T.S. Ingenieria Informatica C/ Periodista Daniel Saucedo Aranda s/n, University of Granada, 18071, Granada, Spain

    David Pelta

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Sierocinski, T., Le Béchec, A., Théret, N., Petritis, D. (2008). Semantic Distillation: A Method for Clustering Objects by their Contextual Specificity. In: Krasnogor, N., Nicosia, G., Pavone, M., Pelta, D. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2007). Studies in Computational Intelligence, vol 129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78987-1_39

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  • DOI: https://doi.org/10.1007/978-3-540-78987-1_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78986-4

  • Online ISBN: 978-3-540-78987-1

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