Oyana, Tonny J.Achenie, Luke E. K.Heo, Joon2014-06-122014-06-122012Tonny J. Oyana, Luke E. K. Achenie, and Joon Heo, "The New and Computationally Efficient MIL-SOM Algorithm: Potential Benefits for Visualization and Analysis of a Large-Scale High-Dimensional Clinically Acquired Geographic Data," Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 683265, 14 pages, 2012. doi:10.1155/2012/6832651748-670Xhttp://hdl.handle.net/10919/48910The 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.application/pdfenCreative Commons Attribution 3.0 UnportedSelf-organizing mapsPID controlPollutionDesignMathematical & computational biologyArticle - RefereedCopyright © 2012 Tonny J. Oyana et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.http://www.hindawi.com/journals/cmmm/2012/683265/cta/https://doi.org/10.1155/2012/683265