A Hierarchical Visualization Tool to Analyse the Thermal Evolution of Construction Materials

  • Emilio Corchado
  • Pedro Burgos
  • María del Mar Rodríguez
  • Verónica Tricio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3190)


This paper proposes a new visualization tool based on feature selection and the identification of underlying factors. The goal of this method is to visualize and extract information from complex and high dimensional data sets. The model proposed is an extension of Maximum Likelihood Hebbian Learning based on a family of cost functions, which maximizes the likelihood of identifying a specific distribution in the data while minimizing the effect of outliers. We present and demonstrate a hierarchical extension method which provides an interactive method for visualizing and identifying possibly hidden structure in the dataset. We have applied this method to investigate and visualize the thermal evolution of several frequent construction materials under different thermal and humidity environmental conditions.


Construction Material Thermal Evolution Transitory State Thermal Flux Projection Pursuit 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Charles, D.: Unsupervised Artificial Neural Networks for the Identification of Multiple Causes in Data. PhD thesis, University of Paisley (1999)Google Scholar
  2. 2.
    Fyfe, C.: Negative Feedback as an Organising Principle for Artificial Neural Networks, PhD Thesis, Strathclyde University (1995)Google Scholar
  3. 3.
    Fyfe, C., Corchado, E.: Maximum Likelihood Hebbian Rules.In: European Symposium on Artificial Neural Networks (2002)Google Scholar
  4. 4.
    Friedman, J., Tukey, J.: A Projection Pursuit Algorithm for Exploratory Data Analysis. IEEE Transaction on Computers 23, 881–890 (1974)zbMATHCrossRefGoogle Scholar
  5. 5.
    Corchado, E., Fyfe, C.: Orientation Selection Using Maximum Likelihood Hebbian Learning. International Journal of Knowledge-Based Intelligent Engineering Systems 7(2) (2003) ISSN: 1327-2314. Brighton, United KingdomGoogle Scholar
  6. 6.
    Corchado, E., MacDonald, D., Fyfe, C.: Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit, Data Mining and Knowledge Discovery,vol. 8(3). Klu-wer Academic Publishing, Boston (May 2004)Google Scholar
  7. 7.
    Tricio, V., Viloria, R.: Microclimatic Study in a Historical Building: Protection and Conservation of the Cultural Heritage of the Mediterranean Cities. Topic: Environmental parameters of the Mediterranean Basin, 67–70 (2002)Google Scholar
  8. 8.
    Corchado, E., Burgos, P., Rodríguez, M., Tricio, V.: An Unsupervised Neural Model to Analyze Thermal Properties of Construction Materials. In: Proceedings, Part II,ICCS 2004, 4th International Conference, Kraków, Poland, pp. 204–211 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Emilio Corchado
    • 1
  • Pedro Burgos
    • 1
  • María del Mar Rodríguez
    • 2
  • Verónica Tricio
    • 2
  1. 1.Department of Civil EngineeringUniversity of BurgosBurgosSpain
  2. 2.Department of PhysicsUniversity of BurgosBurgosSpain

Personalised recommendations