The semantic connectivity map: an adapting self-organising knowledge discovery method in data bases. Experience in gastro-oesophageal reflux disease

We describe here a new mapping method able to find out connectivity traces among variables thanks to an artificial adaptive system, the Auto Contractive Map (AutoCM), able to define the strength of the associations of each variable with all the others in a dataset. After the training phase, the weig...

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Bibliographic Details
Published inInternational journal of data mining and bioinformatics Vol. 2; no. 4; p. 362
Main Authors Buscema, Massimo, Grossi, Enzo
Format Journal Article
LanguageEnglish
Published Switzerland 01.01.2008
Subjects
Adult
Algorithms
Artificial Intelligence
Automatic Data Processing
Computational Biology - methods
Data Interpretation, Statistical
Databases, Factual
Female
Gastroesophageal Reflux - etiology
Humans
Information Storage and Retrieval
Male
Neural Networks (Computer)
Software
Online AccessGet more information

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Summary:We describe here a new mapping method able to find out connectivity traces among variables thanks to an artificial adaptive system, the Auto Contractive Map (AutoCM), able to define the strength of the associations of each variable with all the others in a dataset. After the training phase, the weights matrix of the AutoCM represents 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 complex and specific diseases description.
ISSN:1748-5673
DOI:10.1504/IJDMB.2008.022159