Clustering of City Housing Facilities Based on Self-Organizing Maps

Abstract:

The algorithm for clustering based on neural network modeling using T. Kohonen's self-organizing maps for the analysis of the housing stock is considered. This analysis of housing stock is required for the planning of complex reproduction of housing and major repairs regional programs development. The mechanism of self-organization is submitted. The representative sample clustering of the housing stock is produced. Its result is 16 groups of objects with a high level of internal similarity. The basic advantages of this approach for monitoring and analysis of the city housing stock are described.

• Periodical: Applied Mechanics and Materials (Volumes 725-726)
• Pages: 1057-1062
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.725-726.1057
• Online since: January 2015
• Authors: Tatiana Simankina*, Olga Popova
• Keywords: Clustering, Current Repairs of the Building, Housing Stock Analysis, Major Repairsof the Building, Neural Network Modeling, Reproduction of Housing, Self-Organizing Maps

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