Applying Clustering Techniques to Retrieve Housing Units from a Repository

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.

[1]  Scott Curland Chase,et al.  Categorisation of designs according to preference values for shape rules , 2008 .

[2]  H. L. Le Roy,et al.  Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability; Vol. IV , 1969 .

[3]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[4]  Yi Zhang,et al.  Pattern Discovering of Regional Traffic Status with Self-Organizing Maps , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[5]  Casebook , 1993, Medical History. Supplement.

[6]  Yi Pan,et al.  Improved K-means clustering algorithm for exploring local protein sequence motifs representing common structural property , 2005, IEEE Transactions on NanoBioscience.

[7]  Babak Nadjar Araabi,et al.  Finding the Number of Clusters in a Dataset Using an Information Theoretic Hierarchical Algorithm , 2006, 2006 13th IEEE International Conference on Electronics, Circuits and Systems.

[8]  Teuvo Kohonen.  Self-organizing maps of massive document collections , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[9]  Andrea Resmini,et al.  FaceTag: Integrating Bottom-up and Top-down Classification , 2006, SWAP.

[10]  Sergio M. Savaresi,et al.  Cluster Selection in Divisive Clustering Algorithms , 2002, SDM.

[11]  Philip Steadman,et al.  Architectural morphology : an introduction to the geometry of building plans , 1983 .

[12]  Chieh-Jen and Chiu Mao-Lin Lin.  Smart Semantic Query of Design Information in a Case Library , 2003 .

[13]  R. Michalski,et al.  Learning from Observation: Conceptual Clustering , 1983 .

[14]  Victor J. Rayward-Smith,et al.  Internal quality measures for clustering in metric spaces , 2008, Int. J. Bus. Intell. Data Min..

[15]  Owen Bishop.  Electronics - Circuits and Systems , 2007 .

[16]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[17]  L. Madrazo,et al.  Integrating floor plan layout generation processes within an open and collaborative system to design and build customized housing , 2009 .

[18]  J. Michael Gerzso,et al.  Automatic Generation of Layouts of an Utzon Housing System via the Internet , 2001, Proceedings of the 21st Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA).

[19]  Qingshan Deng,et al.  Combining self-organizing map and K-means clustering for detecting fraudulent financial statements , 2009, 2009 IEEE International Conference on Granular Computing.

[20]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[21]  Robert J. Krawczyk,et al.  A Choice Model of Consumer Participatory Design for Modular Houses , 2007 .

[22]  John R. Anderson,et al.  MACHINE LEARNING An Artificial Intelligence Approach , 2009 .

[23]  Fernando Bação,et al.  Self-organizing Maps as Substitutes for K-Means Clustering , 2005, International Conference on Computational Science.

[24]  Sergei Vassilvitskii,et al.  k-means++: the advantages of careful seeding , 2007, SODA '07.

[25]  Catherine A. Sugar,et al.  Finding the Number of Clusters in a Dataset , 2003 .

[26]  Syed Sibte Raza Abidi,et al.  Data Mining Using Self-Organizing Kohonen Maps: A Technique for Effective Data Clustering & Visualization , 1999, IC-AI.