An Improving Algorithm Based on SOM Clustering and its Applications

Abstract:

SOM (Self-organizing Map) algorithm is a clustering method basing on non-supervision condition. The paper introduces an improved algorithm based on SOM neural network clustering. It proposes SOM’s basic theory on data clustering. For SOM’s practical problems in applications, the algorithm also improved the selection of initial weights and the scope of neighborhood parameters. Finally, the simulation results in Matlab prove that the improved clustering algorithm improve the correct rate and computational efficiency of data clustering and to make the convergence speed better.

• Periodical: Advanced Materials Research (Volumes 655-657)
• Pages: 1000-1004
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.655-657.1000
• Online since: January 2013
• Authors: Chen Guang Yan, Yu Jing Liu, Jin Hui Fan
• Keywords: Data Clustering, Initial Weights, Scope of Neighborhood, Self-Organizing Maps

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