title: The new and computationally efficient MIL-SOM algorithm: potential benefits for visualization and analysis of a large-scale high-dimensional clinically acquired geographic data.
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.
authors.affiliation: GIS Research Laboratory for Geographic Medicine, Department of Geography and Environmental Resources, Southern Illinois University, 1000 Faner Drive, MC 4514, Carbondale, IL 62901, USA. tjoyana@siu.edu
authors.forename: Tonny J
authors.initials: TJ
authors.lastname: Oyana
authors.forename: Luke E K
authors.initials: LE
authors.lastname: Achenie
authors.forename: Joon
authors.initials: J
authors.lastname: Heo
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001249:Asthma / Q000453:epidemiology; D002641:Chicago / Q000453:epidemiology; D002648:Child; D040362:Geographic Information Systems; D006801:Humans; D007854:Lead / Q000097:blood; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D015995:Prevalence
chemical_list: D007854:Lead