Abstract
Self Organizing Map (SOM) is a significant algorithmic methodology to visualize data spaces of larger dimensions. Accurate analysis of the input data requires a well-trained SOM. Many measures are there in practice to analyse the quality of the map. One of the most commonly used measure is Quantization Error. A trained SOM grid with minimum quantization error may not be topologically well preserved. The quality of Topology preservation is measured using Topographic Error. Choosing SOM dimension for the map training procedure is not straight forward as it may not guarantee a map with minimum of quantization and topographic errors. This paper proposes an SOM-Harmony Hybrid algorithm that will compute the optimal dimension of the SOM grid with minimum values of topographic and quantization errors.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
