A novel adapting mapping method for emergent properties discovery in data bases: experience in medical field
Massimo Buscema, Enzo Grossi · 2007
We describe here a new mapping method able to find out connectivity traces among variables thanks to an original mathematical approach. This method is based on an artificial adaptive system able to define the strength of the associations of each variable with all the others in any dataset, the Auto Contractive Map (AutoCM). After the training phase, the weights matrix of the AutoCM represents the warped landscape of the dataset. We apply a simple filter to the weights matrix of AutoCM system to show the map of the main connections between the variables. The example of gastro-oesophageal reflux disease data base is extremely useful to figure out how this new approach can help to re design the overall structure of factors related to a specific disease description. This new form of data mining can be expected to contribute to a better understanding of complexity of some wicked diseases.