Applying Clustering Techniques to Retrieve Housing Units from a Repository

  • Álvaro Sicilia
  • Leandro Madrazo
  • Mar González


The purpose of BARCODE HOUSING SYTEM, a research project developed over the last four years, has been to create an Internet-based system which facilitates the interaction of the different actors involved in the design, construction and use of affordable housing built with industrialized methods. One of the components of the system is an environment which enables different users – architects, clients, developers – to retrieve the housing units generated by a rule-based engine and stored in a repository. Currently, the repository contains over 10,000 housing units. In order to access this information, we have developed clustering techniques based on self-organizing maps and k-means methods.


Cluster Technique Housing Unit Information Retrieval System Floor Plan Architect User 
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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  1. 1.
    Chien, S.F., Shih, S.G.: A Web Environment to Support User Participation in the Development of Apartment Buildings. In: Special Focus Symposium on WWW as the Framework for Collaboration, InterSymp., Baden-Baden, Germany, pp. 225–231 (2000)Google Scholar
  2. 2.
    Gerzso, J.M.: Automatic generation of layouts of an Utzon housing system via the Internet. Reinventing the Discourse - How Digital Tools Help Bridge and Transform Research, Education and Practice in Architecture. In: 21st Annual Conference of the ACADIA, Buffalo, New York, pp. 202–211 (2001)Google Scholar
  3. 3.
    Huang, J.C., Krawczyk, R.: A Choice Model of Consumer Participatory Design for Modular Houses. In: 25th International Conference Aided Architectural Design in Europe, Germany, pp. 679–686 (2007)Google Scholar
  4. 4.
    Madrazo, L., Sicilia, A., González, M., Martin, A.: Integrating floor plan layout generation processes within an open and collaborative system to design and build customized housing. In: Tidafi, T., Dorta, T. (eds.) Joining Languages, Cultures and Visions: CAADFutures, pp. 656–670 (2009)Google Scholar
  5. 5.
    Deng, Q.: Combining Self-Organizing Map and K-Means Clustering for Detecting Fraudulent Financial Statements. In: IEEE International Conference on Granular Computing, GRC 2009, pp. 126–131 (2009)Google Scholar
  6. 6.
    Chen, Y., Zhang, Y., Hu, J., Yao, D.: Pattern Discovering of Regional Traffic Status with Self-Organizing Maps. In: Intelligent Transportation Systems Conference, ITSC 2006, pp. 647–652. IEEE, Los Alamitos (2006)CrossRefGoogle Scholar
  7. 7.
    Kohonen, T.: Self organization of a massive document collection. IEEE Transactions on Neural Networks 11(3), 574–585 (2000)CrossRefGoogle Scholar
  8. 8.
    Zhong, W.: Improved K-Means Clustering Algorithm for Exploring Local Protein Sequence Motifs Representing Common Structural Property. IEEE Transactions on NanoBioscience 4(3), 255–265 (2005)CrossRefGoogle Scholar
  9. 9.
    Lin, C., Chiu, M.: Smart Semantic Query of Design Information in a Case Library. Digital Design: Research and Practice. In: 10th International Conference on CAADFutures, pp. 125–135 (2003)Google Scholar
  10. 10.
    Inanc, B.S.: Casebook. An Information Retrieval System for Housing Floor Plans. In: CAADRIA 2000, 5th Conference on Computer Aided Architectural Design Research in Asia, Singapore, pp. 389–398 (2000)Google Scholar
  11. 11.
    Lim, S., Prats, M., Chase, S., Garner, S.: Categorisation of Designs According to Preference Values for Shape Rules. In: Gero, J.S., Goel, A.K. (eds.) Design Computing and Cognition, pp. 41–60. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Steadman, J.P.: Architectural Morphology. Pion Limited, London (1983)Google Scholar
  13. 13.
    Quintarelli, E.: Facetag: Integrating Bottom-up and Top-down Classification in a Social Tagging System. Las Vegas IA Summit (2007)Google Scholar
  14. 14.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. ACM Press, Addison-Wesley, New York (1999)Google Scholar
  15. 15.
    Savaresi, S.: Cluster selection in divisive clustering algorithms. In: 2nd SIAM ICDM, Arlington, VA, USA, pp. 299–314 (2002)Google Scholar
  16. 16.
    Jain, A.K.: Data clustering: A Review. ACM Computing Surveys 31(3) (1999)Google Scholar
  17. 17.
    Kohonen, T.: Self-Organizing Maps. Springer, New York (1995)Google Scholar
  18. 18.
    Ong, J.: Data Mining Using Self-Organizing Kohonen maps: A Technique for Effective Data Clustering & Visualization. In: International Conference on Artificial Intelligence (IC-AI), Las Vegas (1999)Google Scholar
  19. 19.
    MacQueen, J.B.: Some Methods for classification and Analysis of Multivariate Observations. In: 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297. University of California Press, Berkeley (1967)Google Scholar
  20. 20.
    Arthur, D., Vassilvitski, S.: K-Means++: The advantages of careful seeding. In: Bansal, N., Pruhs, K., Stein, C. (eds.) 18th Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, Louisiana, pp. 1027–1035 (2007)Google Scholar
  21. 21.
    Singhal, A.: Modern Information Retrieval: A Brief Overview. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering 24(4), 35–43 (2001)Google Scholar
  22. 22.
    Aghagolzadeh, M.: Finding the number of clusters in a dataset using information theoretic hierarchical algorithm. Electronics, Circuits and Systems. In: ICECS. 13th IEEE International Conference, Nice, France, pp. 1336–1339 (2006)Google Scholar
  23. 23.
    Michalski, R., Stepp, R.: Learning from observation: Conceptual clustering. Machine Learning: An Artificial Intelligence Approach, pp. 471–498. Morgan Kaufmann, Los Altos (1986)Google Scholar
  24. 24.
    Baçao, F., Lobo, V., Painho, M.: Self-organizing Maps as Substitutes for K-Means Clustering. In: 5th International Conference Computational Science - ICCS, Atlanta, GA, USA (2005)Google Scholar
  25. 25.
    Nguyen, Q.H., Rayward-Smith, V.J.: Internal quality measures for clustering in metric spaces. Int. J. Business Intelligence and Data Mining 3(1), 4–29 (2008)CrossRefGoogle Scholar

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© Springer Netherlands 2011

Authors and Affiliations

  • Álvaro Sicilia
    • 1
  • Leandro Madrazo
    • 1
  • Mar González
    • 1
  1. 1.ARC Enginyeria i Arquitectura La SalleSpain

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