the new and computationally efficient mil-som algorithm: potential benefits for visualization and analysis of a large-scale high-dimensional clinically acquired geographic data

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ID: 258650
2012
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Abstract
The objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen’s SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this improvement translates to faster convergence. The basic idea is primarily motivated by the urgent need to develop algorithms with the competence to converge faster and more efficiently than conventional techniques. The MIL-SOM algorithm is tested on four training geographic datasets representing biomedical and disease informatics application domains. Experimental results show that the MIL-SOM algorithm provides a competitive, better updating procedure and performance, good robustness, and it runs faster than Kohonen’s SOM.
Reference Key
oyana2012computationalthe Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Tonny J. Oyana;Luke E. K. Achenie;Joon Heo
Journal advanced functional materials
Year 2012
DOI
10.1155/2012/683265
URL
http://dx.doi.org/10.1155/2012/683265
Keywords
medical informatics computer applications to medicine

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